{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "import warnings\n",
    "\n",
    "notebook.tqdm.pandas()\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
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       "model_id": "50e843a7c2e94c288705cd1b696a6456",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.877        4.2    4.20       4174\n",
      "          1,3,3           8.5          0.882        9.1    2.17       2711\n",
      "        1,3,3,3          17.8          0.860       13.8    1.52       1619\n",
      "      1,3,3,3,3          36.4          0.833       22.3    1.62        845\n",
      "    1,3,3,3,3,3          74.7          0.861       36.4    1.63        402\n",
      "  1,3,3,3,3,3,3         118.2          0.895       38.5    1.06        182\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6588\n",
      "          3,3,3           9.0          0.960       23.5    1.55       5166\n",
      "        3,3,3,3          19.1          0.941       43.5    1.85       3542\n",
      "      3,3,3,3,3          35.9          0.931       53.0    1.22       1976\n",
      "    3,3,3,3,3,3          65.0          0.928       97.6    1.84       1149\n",
      "  3,3,3,3,3,3,3         100.9          0.947      142.9    1.46        537\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.75        131\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       39.0    3.22       7515\n",
      "          4,3,3          17.8          0.963       56.5    1.45       5307\n",
      "        4,3,3,3          32.1          0.952       82.6    1.46       3032\n",
      "      4,3,3,3,3          46.3          0.956      125.4    1.52       1380\n",
      "    4,3,3,3,3,3          67.5          0.956      115.3    0.92        534\n",
      "  4,3,3,3,3,3,3          78.3          0.978      117.5    1.02        253\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.51        166\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "\n",
    "\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = (df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)\n",
    "df['helper'] = df['helper'].astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "from itertools import accumulate\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2, 0.2, 0.2, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.15, 0.8)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from (self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=3, lr=4e-2, batch_size=512) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "        pbar.close()\n",
    "\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            print(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            print(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "441d7433a4ca457e93f6732bf1bd5ba0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/224893 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 178992 TEST: 45901\n",
      "dataset built\n",
      "Loss in trainset: 0.3473\n",
      "Loss in testset: 0.2982\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2a3d99a28874c29b971db609c9f3109",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/86855 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.9578, 2.1792, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dd551cc0f484af585a69ec1e7a8af55",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/808105 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3453\n",
      "Loss in testset: 0.2901\n",
      "iteration: 80896\n",
      "w: [0.9535, 2.2152, 4.6675, 1.7343, 1.5027, 0.05, 1.2747, 0.3196, 0.9378, 1.9111, 0.4548, 0.3956, 1.5832]\n",
      "iteration: 161792\n",
      "w: [0.9535, 2.2152, 4.1024, 1.5797, 1.5747, 0.05, 1.1317, 0.1845, 0.8386, 1.8257, 0.5976, 0.5482, 1.0059]\n",
      "Loss in trainset: 0.3298\n",
      "Loss in testset: 0.2768\n",
      "iteration: 242517\n",
      "w: [0.9535, 2.2152, 3.979, 1.7803, 1.7647, 0.05, 1.2224, 0.15, 0.9665, 1.8467, 0.5856, 0.438, 1.1586]\n",
      "iteration: 323413\n",
      "w: [0.9535, 2.2152, 3.8581, 1.7779, 1.5566, 0.05, 1.1459, 0.15, 0.861, 1.9536, 0.4619, 0.6275, 0.9929]\n",
      "Loss in trainset: 0.3280\n",
      "Loss in testset: 0.2744\n",
      "iteration: 404138\n",
      "w: [0.9535, 2.2152, 3.6896, 1.5786, 1.5389, 0.05, 1.2381, 0.159, 0.9386, 1.9042, 0.508, 0.5783, 0.9919]\n",
      "iteration: 485034\n",
      "w: [0.9535, 2.2152, 3.8286, 1.8391, 1.6857, 0.05, 1.2042, 0.15, 0.9058, 1.9121, 0.4883, 0.5867, 0.9089]\n",
      "Loss in trainset: 0.3275\n",
      "Loss in testset: 0.2740\n",
      "iteration: 565759\n",
      "w: [0.9535, 2.2152, 3.7698, 1.7131, 1.6073, 0.05, 1.1856, 0.15, 0.8782, 1.9069, 0.4818, 0.5289, 0.8997]\n",
      "iteration: 646655\n",
      "w: [0.9535, 2.2152, 3.7594, 1.7282, 1.5852, 0.05, 1.1792, 0.1634, 0.8618, 1.8741, 0.5109, 0.5682, 0.8879]\n",
      "Loss in trainset: 0.3274\n",
      "Loss in testset: 0.2742\n",
      "iteration: 727380\n",
      "w: [0.9535, 2.2152, 3.7435, 1.7195, 1.5828, 0.05, 1.2051, 0.1501, 0.8846, 1.8969, 0.4851, 0.5632, 0.9183]\n",
      "iteration: 808276\n",
      "w: [0.9535, 2.2152, 3.7479, 1.7202, 1.5802, 0.05, 1.1928, 0.1559, 0.8715, 1.8885, 0.4934, 0.5622, 0.9112]\n",
      "Loss in trainset: 0.3273\n",
      "Loss in testset: 0.2740\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 182580 TEST: 42313\n",
      "dataset built\n",
      "Loss in trainset: 0.3263\n",
      "Loss in testset: 0.3844\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c756deb769104810a725c323f5c89e38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/104865 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [2.032, 2.5142, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "315d089704514465b084305276f0c0cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/808035 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3225\n",
      "Loss in testset: 0.3905\n",
      "iteration: 80896\n",
      "w: [2.0232, 2.5334, 4.533, 1.8207, 1.3314, 0.05, 1.2207, 0.15, 0.812, 1.7823, 0.6151, 0.4824, 1.4222]\n",
      "iteration: 161792\n",
      "w: [2.0232, 2.5334, 4.0951, 1.6651, 1.5376, 0.05, 1.216, 0.156, 0.8716, 1.7283, 0.6531, 0.4932, 1.1484]\n",
      "Loss in trainset: 0.3074\n",
      "Loss in testset: 0.3784\n",
      "iteration: 242503\n",
      "w: [2.0232, 2.5334, 4.0664, 1.9838, 1.5245, 0.05, 1.2853, 0.1615, 0.933, 1.8737, 0.5665, 0.5504, 1.6494]\n",
      "iteration: 323399\n",
      "w: [2.0232, 2.5334, 3.8497, 1.8295, 1.3532, 0.05, 1.1136, 0.15, 0.7827, 1.8511, 0.5701, 0.589, 1.2287]\n",
      "Loss in trainset: 0.3052\n",
      "Loss in testset: 0.3753\n",
      "iteration: 404110\n",
      "w: [2.0232, 2.5334, 3.7697, 1.726, 1.3997, 0.05, 1.2219, 0.1877, 0.8726, 1.8991, 0.503, 0.6077, 1.2186]\n",
      "iteration: 485006\n",
      "w: [2.0232, 2.5334, 3.8768, 1.9681, 1.5152, 0.05, 1.2053, 0.15, 0.8586, 1.8266, 0.5758, 0.6061, 1.197]\n",
      "Loss in trainset: 0.3048\n",
      "Loss in testset: 0.3748\n",
      "iteration: 565717\n",
      "w: [2.0232, 2.5334, 3.9208, 1.8291, 1.6582, 0.05, 1.209, 0.1683, 0.8446, 1.8453, 0.5475, 0.5991, 1.1502]\n",
      "iteration: 646613\n",
      "w: [2.0232, 2.5334, 3.8519, 1.826, 1.5003, 0.05, 1.2072, 0.1525, 0.8304, 1.8265, 0.5602, 0.593, 1.1504]\n",
      "Loss in trainset: 0.3048\n",
      "Loss in testset: 0.3748\n",
      "iteration: 727324\n",
      "w: [2.0232, 2.5334, 3.8391, 1.7756, 1.5062, 0.05, 1.2031, 0.15, 0.8213, 1.839, 0.5434, 0.6221, 1.1594]\n",
      "iteration: 808220\n",
      "w: [2.0232, 2.5334, 3.8406, 1.775, 1.5052, 0.05, 1.1988, 0.15, 0.8159, 1.8358, 0.5461, 0.6169, 1.1535]\n",
      "Loss in trainset: 0.3048\n",
      "Loss in testset: 0.3746\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 185232 TEST: 39661\n",
      "dataset built\n",
      "Loss in trainset: 0.3379\n",
      "Loss in testset: 0.3343\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5448eabdd4e043449fd621e69110a7ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/143650 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.1055, 2.9948, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29e16c60491d40dfa4368d051542397a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/782510 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3335\n",
      "Loss in testset: 0.3320\n",
      "iteration: 78336\n",
      "w: [1.0842, 3.0078, 4.3113, 1.8521, 1.5062, 0.05, 1.2822, 0.2291, 0.8949, 1.9329, 0.4781, 0.5167, 1.3329]\n",
      "iteration: 156672\n",
      "w: [1.0842, 3.0078, 4.1012, 1.9594, 1.5742, 0.05, 1.2654, 0.1653, 0.9242, 1.9278, 0.5898, 0.6252, 1.7894]\n",
      "Loss in trainset: 0.3169\n",
      "Loss in testset: 0.3139\n",
      "iteration: 234838\n",
      "w: [1.0842, 3.0078, 3.9052, 1.6847, 1.536, 0.05, 1.2505, 0.15, 0.9372, 1.9672, 0.5713, 0.5328, 1.7558]\n",
      "iteration: 313174\n",
      "w: [1.0842, 3.0078, 3.992, 1.8023, 1.5018, 0.05, 1.1232, 0.2328, 0.815, 2.0483, 0.502, 0.624, 1.6381]\n",
      "Loss in trainset: 0.3187\n",
      "Loss in testset: 0.3158\n",
      "iteration: 391340\n",
      "w: [1.0842, 3.0078, 3.8188, 1.8542, 1.2998, 0.0661, 1.1995, 0.15, 0.9041, 1.9691, 0.5809, 0.582, 1.3692]\n",
      "iteration: 469676\n",
      "w: [1.0842, 3.0078, 3.9318, 1.7295, 1.7756, 0.0578, 1.2137, 0.15, 0.8996, 1.9193, 0.6108, 0.5333, 1.3501]\n",
      "Loss in trainset: 0.3172\n",
      "Loss in testset: 0.3142\n",
      "iteration: 547842\n",
      "w: [1.0842, 3.0078, 3.8944, 1.6899, 1.6391, 0.05, 1.2112, 0.1649, 0.8882, 1.9916, 0.5348, 0.6174, 1.4013]\n",
      "iteration: 626178\n",
      "w: [1.0842, 3.0078, 3.8962, 1.7171, 1.5928, 0.05, 1.2028, 0.1565, 0.869, 1.9604, 0.5617, 0.5781, 1.365]\n",
      "Loss in trainset: 0.3166\n",
      "Loss in testset: 0.3132\n",
      "iteration: 704344\n",
      "w: [1.0842, 3.0078, 3.884, 1.707, 1.5588, 0.05, 1.202, 0.15, 0.8643, 1.9573, 0.5628, 0.5951, 1.3543]\n",
      "iteration: 782680\n",
      "w: [1.0842, 3.0078, 3.8902, 1.7067, 1.5653, 0.05, 1.1962, 0.1512, 0.8576, 1.9507, 0.5686, 0.5864, 1.347]\n",
      "Loss in trainset: 0.3166\n",
      "Loss in testset: 0.3132\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 176384 TEST: 48509\n",
      "dataset built\n",
      "Loss in trainset: 0.3398\n",
      "Loss in testset: 0.3279\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "749a0411a04c42328c2f61ee8c4b48ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/99180 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0214, 3.1445, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7e7701676794c31bfe9ddbeaff7d0ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/782740 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3356\n",
      "Loss in testset: 0.3241\n",
      "iteration: 78336\n",
      "w: [1.0535, 3.1968, 4.7289, 1.6698, 1.8162, 0.05, 1.1609, 0.1864, 0.8277, 1.8114, 0.5551, 0.6435, 0.9638]\n",
      "iteration: 156672\n",
      "w: [1.0535, 3.1968, 4.2015, 1.7249, 1.6485, 0.05, 1.2422, 0.1737, 0.9604, 1.9207, 0.446, 0.4591, 1.1868]\n",
      "Loss in trainset: 0.3193\n",
      "Loss in testset: 0.3059\n",
      "iteration: 234884\n",
      "w: [1.0535, 3.1968, 3.8513, 1.57, 1.5578, 0.05, 1.2847, 0.15, 1.0047, 1.799, 0.562, 0.5178, 1.1417]\n",
      "iteration: 313220\n",
      "w: [1.0535, 3.1968, 3.8397, 1.5568, 1.4805, 0.05, 1.079, 0.1766, 0.8101, 1.9026, 0.4517, 0.5827, 1.0731]\n",
      "Loss in trainset: 0.3195\n",
      "Loss in testset: 0.3057\n",
      "iteration: 391432\n",
      "w: [1.0535, 3.1968, 3.8141, 1.7373, 1.5194, 0.05, 1.157, 0.15, 0.8922, 1.8546, 0.4956, 0.6232, 0.8432]\n",
      "iteration: 469768\n",
      "w: [1.0535, 3.1968, 3.7371, 1.4715, 1.531, 0.05, 1.1523, 0.1575, 0.8724, 1.7991, 0.522, 0.5134, 0.8853]\n",
      "Loss in trainset: 0.3194\n",
      "Loss in testset: 0.3054\n",
      "iteration: 547980\n",
      "w: [1.0535, 3.1968, 3.758, 1.6345, 1.4685, 0.05, 1.1649, 0.15, 0.868, 1.8602, 0.4576, 0.589, 0.9025]\n",
      "iteration: 626316\n",
      "w: [1.0535, 3.1968, 3.7764, 1.5991, 1.5512, 0.05, 1.1689, 0.1575, 0.8627, 1.8293, 0.4756, 0.562, 0.8733]\n",
      "Loss in trainset: 0.3188\n",
      "Loss in testset: 0.3051\n",
      "iteration: 704528\n",
      "w: [1.0535, 3.1968, 3.7783, 1.5915, 1.5633, 0.05, 1.1621, 0.15, 0.8515, 1.8381, 0.4635, 0.5626, 0.8744]\n",
      "iteration: 782864\n",
      "w: [1.0535, 3.1968, 3.7764, 1.5864, 1.5595, 0.05, 1.1631, 0.15, 0.8518, 1.8326, 0.4686, 0.5595, 0.8685]\n",
      "Loss in trainset: 0.3188\n",
      "Loss in testset: 0.3051\n"
     ]
    },
    {
     "data": {
      "image/png": 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Rq1cvnnrqKdq0acOnn35KSkrKacczgv6ZL+fFZDJxY5cWLEm5mqdu6EhII2827y/k3pk/cOfb37Nm92GjSxQRkToWFRVFRkYGO3bs4ODBgzz00EMcOnSIO++8k1WrVrF161YWLVpEcnIyZWVlZGRk8Nxzz/HDDz+Qk5PDvHnzOHDgAB06dHAdb82aNWzcuJGDBw9SUnLhH1qsgCS1wsfLwj1XtmX543148OporF5mvt92iJumrmTURz+y65BBozBERKTOPfbYY1gsFjp27EjTpk0pLi5m5cqVlJWVcc0119CpUydGjx5NSEgIZrOZoKAgvvnmG66//nrat2/P//3f//Hyyy9z3XXXATB8+HAuvvhi4uPjadq0KStXrrzg16Rh/jXUkIb518Sew8d4+auNfPrjHhwO8LaYGNozilF/iCGkUfWHaoqINBS/NzRdzk1tDPNXC5LUiZYhfrxyexyfj7ySK2OaUFLm4N1vt9P7xaW8tXwrx0uMvbcsIiLyexSQpE5d2jKY2fcl8K97ehAbEUjB8VJSv9xA35eX8+mPu7Hb1YApIiLuRwFJLoje7ZvyxcNX8Y/butA82Jc9h48xZu5P3Dj1W77dfPDsBxAREbmAFJDkgrGYTQzs1oqljyXyxLUXE+jjxbq9BQx5N4Nh72Wyfl+B0SWKiIgACkhiAF9vCyMSY1j+RB/uviIKL7OJ5ZsOcP3rK3j8Pz+xL/+Y0SWKiBhGY6fOX218hwpIYphQfysTb7qEJSlXM6BTcxwO+E/Wbvr8YxkvLdpAwfEL/9wLERGjeHt7A3D0qB6Lcr7Kv8Py77QmNMy/hjTMv/b9mPMbzy1Yz6odzvfxhPpbefgPMdyV0Aarl7K8iHi+ffv2cfjwYZo1a0ajRo30NoJz5HA4OHr0KPv37yckJITmzZuftk11f78VkGpIAaluOBwOFv+Sx/MLN7DtQBEAUWGNeOLaWK67NEL/sRARj+ZwOMjNzeXw4cNGl1KvhYSEEBFx5t8MBaQ6poBUt0rL7MxZtYvJSzZxsLAYgMtahzD++g7ER4UaXJ2ISN0qKysz5PUansDb2xuLxVLlegWkOqaAdGEU2kp5+5ttzPhmG8dOPFyy/yXhPHFtLNFNAwyuTkRE6hsFpDqmgHRh7S84zqtLNjN3VQ52h/ORAXf2iOSRvu1pGuhjdHkiIlJPKCDVMQUkY2zOO8ILCzewZP1+APytFt67uzsJ7cIMrkxEROoDvYtNPNJF4YG8M6w7c+6/nE4tgykqLuOZL9bruSEiIlKrFJCkXrq8XRgz7+mBn7eFtXvyWaHXlYiISC1yi4A0bdo0oqKi8PX1JSEhgczMzCq3nTdvHvHx8YSEhODv709cXByzZs2qsM3EiROJjY3F39+fxo0bk5SUREZGRoVtoqKiMJlMFabnn3++Tq5P6kaov5W7EloDMG3pFoOrERERT2J4QJo7dy4pKSlMmDCB1atX06VLF/r378/+/fvPuH1oaCjjx48nPT2dNWvWkJycTHJyMosWLXJt0759e6ZOncratWv59ttviYqK4pprruHAgQMVjvX000+zb98+1zRq1Kg6vVapfcOvaoe3xUTG9kP8sOOQ0eWIiIiHMLyTdkJCAt27d2fq1KkA2O12IiMjGTVqFGPHjq3WMbp27cqAAQOYNGnSGdeXd8hasmQJffv2BZwtSKNHj2b06NE1qludtN3HuHlr+ChzF3+IbcZ7d3c3uhwREXFj9aKTdnFxMVlZWSQlJbmWmc1mkpKSSE9PP+v+DoeDtLQ0Nm7cSO/evas8x4wZMwgODqZLly4V1j3//POEhYVx2WWX8dJLL1FaWlrluWw2GwUFBRUmcQ8P9I7GbIKvN+xn3d58o8sREREPYGhAOnjwIGVlZYSHh1dYHh4eTm5ubpX75efnExAQgNVqZcCAAUyZMoV+/fpV2Gb+/PkEBATg6+vLq6++yuLFi2nSpIlr/cMPP8ycOXNYunQpDzzwAM899xxPPPFEledMTU0lODjYNUVGRtbwqqW2RTXx54bOLQCYvmyrwdWIiIgnMLwPUk0EBgaSnZ3NqlWrePbZZ0lJSWHZsmUVtunTpw/Z2dl89913XHvttdx+++0V+jWlpKSQmJhI586defDBB3n55ZeZMmUKNpvtjOccN24c+fn5rmnXrl11eYlyjv6SGA3AF2v3se1AocHViIhIfWdoQGrSpAkWi4W8vLwKy/Py8oiIiKhyP7PZTExMDHFxcTz66KMMHDiQ1NTUCtv4+/sTExPD5ZdfzrvvvouXlxfvvvtulcdMSEigtLSUHTt2nHG9j48PQUFBFSZxHx2aB5HUoRkOB7y1fJvR5YiISD1naECyWq1069aNtLQ01zK73U5aWho9e/as9nHsdnuVLT/V3SY7Oxuz2UyzZs2qfV5xLyP6xAAw78fd7D18zOBqRESkPvMyuoCUlBSGDRtGfHw8PXr0YPLkyRQVFZGcnAzA0KFDadmypauFKDU1lfj4eKKjo7HZbCxYsIBZs2Yxffp0AIqKinj22We56aabaN68OQcPHmTatGns2bOH2267DYD09HQyMjLo06cPgYGBpKenM2bMGIYMGULjxo2N+SLkvHVt3Zie7cJI3/Yrb6/YxoQbLzG6JBERqacMD0iDBg3iwIEDPPXUU+Tm5hIXF8fChQtdHbdzcnIwm082dBUVFTFixAh2796Nn58fsbGxzJ49m0GDBgFgsVjYsGEDM2fO5ODBg4SFhdG9e3dWrFjBJZc4fzB9fHyYM2cOEydOxGaz0bZtW8aMGUNKSsqF/wKkVj3UJ4b0bb/yUWYOI/vEEBagF9mKiMi5M/w5SPWVnoPknhwOBzdPW8lPu/MZ2SeGx/pfbHRJIiLiRurFc5BEapvJZHL1RZqZvoOC4yUGVyQiIvWRApJ4nH4dwrmoWQBHjpcy+/udRpcjIiL1kAKSeByz2cSIPs7nIr27YjvHissMrkhEROobBSTxSDd2bkGrxn78WlTMv3/QQz1FROTcKCCJR/KymHnwamcr0lvLt1Jcaje4IhERqU8UkMRjDezWiqaBPuzNP87/svcYXY6IiNQjCkjisXy9LQy/qi0A05dvpcyuJ1qIiEj1KCCJR7sroQ3Bft5sO1DEonW5RpcjIiL1hAKSeLQAHy/uviIKgGlLt6DnooqISHUoIInHu/uKKBpZLazbW8DyTQeMLkdEROoBBSTxeI39rQxOaA3AG0u3GlyNiIjUBwpI0iDcd1U7rBYzmTsOkbn9kNHliIiIm1NAkgYhPMiXgfGtAHhj2RaDqxEREXengCQNxoO9ozGbYNnGA/y8J9/ockRExI0pIEmD0TqsETd2aQHA9GXqiyQiIlVTQJIG5S+JztePLPh5H1sPFBpcjYiIuCsFJGlQYiOCSOoQjsMBb6oVSUREqqCAJA3OiD7OVqRPf9zDnsPHDK5GRETckQKSNDhdWzfmiugwSu0O3v5mm9HliIiIG1JAkgbpoT4xAHyUmcPBQpvB1YiIiLtRQJIG6YroMLpEhmArtfPet9uNLkdERNyMApI0SCaTiYdOjGiblb6T/GMlBlckIiLuRAFJGqykDuG0Dw/giK2U2d/vNLocERFxIwpI0mCZzSZGJDr7Ir377XaOFZcZXJGIiLgLBSRp0G7o3JzIUD8OFRUzZ1WO0eWIiIibUECSBs3LYubBq519kWZ8s43iUrvBFYmIiDtQQJIG79aurWgW6MO+/OP898c9RpcjIiJuQAFJGjxfbwvDr2oHwPTlWymzOwyuSEREjKaAJALcldCaYD9vth8s4suf9xldjoiIGEwBSQTw9/EiuVcUANOWbsXhUCuSiEhDpoAkcsLdV0TRyGph/b4Clm08YHQ5IiJiIAUkkRNCGlkZcnkbAKYu3aJWJBGRBkwBSeQU913ZFqvFTNbO38jcfsjockRExCAKSCKnaBbky23xrQCYtmyrwdWIiIhRFJBEKnmgdzQWs4lvNh1g7e58o8sREREDKCCJVNI6rBE3dWkBwBvLthhcjYiIGEEBSeQM/pLofP3IwnW5bNl/xOBqRETkQnOLgDRt2jSioqLw9fUlISGBzMzMKredN28e8fHxhISE4O/vT1xcHLNmzaqwzcSJE4mNjcXf35/GjRuTlJRERkZGhW0OHTrE4MGDCQoKIiQkhHvvvZfCwsI6uT6pf9qHB3JNx3AcDpi+bJvR5YiIyAVmeECaO3cuKSkpTJgwgdWrV9OlSxf69+/P/v37z7h9aGgo48ePJz09nTVr1pCcnExycjKLFi1ybdO+fXumTp3K2rVr+fbbb4mKiuKaa67hwIGTz7YZPHgw69atY/HixcyfP59vvvmG+++/v86vV+qPEX1iAPhv9h52HTpqcDUiInIhmRwGP+wlISGB7t27M3XqVADsdjuRkZGMGjWKsWPHVusYXbt2ZcCAAUyaNOmM6wsKCggODmbJkiX07duX9evX07FjR1atWkV8fDwACxcu5Prrr2f37t20aNHirOcsP2Z+fj5BQUHVvFqpb4a8k8G3Ww4ytGcbnv7jpUaXIyIi56m6v9+GtiAVFxeTlZVFUlKSa5nZbCYpKYn09PSz7u9wOEhLS2Pjxo307t27ynPMmDGD4OBgunTpAkB6ejohISGucASQlJSE2Ww+7VZcOZvNRkFBQYVJPN+IPs6+SHNX7eLAEZvB1YiIyIViaEA6ePAgZWVlhIeHV1geHh5Obm5ulfvl5+cTEBCA1WplwIABTJkyhX79+lXYZv78+QQEBODr68urr77K4sWLadKkCQC5ubk0a9aswvZeXl6EhoZWed7U1FSCg4NdU2RkZE0uWeqZnu3CuKx1CLZSO++t3G50OSIicoEY3gepJgIDA8nOzmbVqlU8++yzpKSksGzZsgrb9OnTh+zsbL777juuvfZabr/99ir7NVXHuHHjyM/Pd027du06z6uQ+sBkMvFQorMv0qz0neQfKzG4IhERuRAMDUhNmjTBYrGQl5dXYXleXh4RERFV7mc2m4mJiSEuLo5HH32UgQMHkpqaWmEbf39/YmJiuPzyy3n33Xfx8vLi3XffBSAiIuK0sFRaWsqhQ4eqPK+Pjw9BQUEVJmkY/hDbjNiIQAptpcxK32F0OSIicgEYGpCsVivdunUjLS3Ntcxut5OWlkbPnj2rfRy73Y7N9vv9Q07dpmfPnhw+fJisrCzX+q+//hq73U5CQsI5XoV4OrPZ5Hou0nsrd3C0uNTgikREpK4ZfostJSWFt99+m5kzZ7J+/Xr+8pe/UFRURHJyMgBDhw5l3Lhxru1TU1NZvHgx27ZtY/369bz88svMmjWLIUOGAFBUVMRf//pXvv/+e3bu3ElWVhb33HMPe/bs4bbbbgOgQ4cOXHvttQwfPpzMzExWrlzJyJEjueOOO6o1gk0angGdmtMmrBGHioqZk6nbqyIins7L6AIGDRrEgQMHeOqpp8jNzSUuLo6FCxe6Om7n5ORgNp/McUVFRYwYMYLdu3fj5+dHbGwss2fPZtCgQQBYLBY2bNjAzJkzOXjwIGFhYXTv3p0VK1ZwySWXuI7zwQcfMHLkSPr27YvZbObWW2/l9ddfv7AXL/WGl8XMg1dHM27eWmZ8s40hl7fB6mX4vy9ERKSOGP4cpPpKz0FqeGylZfR+cSl5BTZeuLUTg7q3NrokERE5R/XiOUgi9YmPl4XhV7UDYPqyrZTZ9W8LERFPpYAkcg7u7NGaxo282fHrURas3Wd0OSIiUkcUkETOgb+PF8m92gIwbekWdIdaRMQzKSCJnKNhPaPwt1rYkHuEpRtr/vBRERFxXwpIIucouJE3Q3q2AWDq12pFEhHxRApIIjVw75VtsXqZWZ1zmIzth4wuR0REapkCkkgNNAv0ZVC884XF05ZuMbgaERGpbQpIIjV0f+92WMwmVmw+yJrdh40uR0REapECkkgNRYY24o9xzlfTvLF0q8HViIhIbVJAEjkPIxKjMZlg4bpcNucdMbocERGpJQpIIuchplkg/TtGADB9uVqRREQ8hQKSyHka0ScagP9l72XXoaMGVyMiIrVBAcnd/PIZLBwHBXqNRX3RuVUIV13UhDK7gxnfbDO6HBERqQUKSO7Eboelz8H3b8BrXWDB45C/x+iqpBoe6hMDwNwfdrH/yHGDqxERkfOlgOROTCa4NhVa94QyG2TOgNfjYP4YOJxjdHXyOxLahtKtTWOKS+28++12o8sREZHzpIDkTkwmiO4DyV/CsM8h6iooK4Yf3oPXL4PPRsEh/fi6I5PJxEMn+iLNTt9J/tESgysSEZHzoYDkjkwmaNsb7p4Pdy+AdolgL4XV/4Ip3eC/I+BXjZhyN30ubkZsRCBFxWXMTN9hdDkiInIeFJDcXVQvGPo/uOcriEkCRxlkfwBT42He/XBgk9EVygnOViRnX6T3Vm6nyFZqcEUiIlJTCkj1ResEGPIJ3Pc1tL8WHHZYMxem9YCP74H9642uUIDrOzUnKqwRh4+W8FGm+o2JiNRXCkj1TatucNdcuH85xN4AOODnT+CNy+HfQyH3Z6MrbNAsZhN/SXT2RXp7xTZspWUGVyQiIjWhgFRftYiDOz6AB7+Fjn90Lvvlf/BmL5gzGPZmG1ldg3bLZa1oHuxLXoGNeav1mAYRkfpIAam+i+gEt/8L/pIOl94KmGDDfJhxNXw4CHZnGV1hg2P1MjP8qnYAvLl8K6VldoMrEhGRc6WA5CnCO8LA9+ChTOg8CExm2LQQ3vkDzL4VdmUaXWGDckePSEL9rez89ShfrNVT0UVE6hsFJE/TtD38aQaM/AHiBoPJAluWwLv94F9/hJ3fGV1hg9DI6sU9vaIAeGPpVux2h7EFiYjIOVFA8lRh0XDzGzAqC7oOBbMXbFsG/7wO3r8Btn8DDv1o16U/94wiwMeLjXlH+HrDfqPLERGRc6CA5OlC28JNU+DhHyH+HjB7w44VMPNG+Of1sHWpglIdCfbz5s892wAwdekWHPqeRUTqDQWkhiKkNdzwKjySDT3uB4sP5HwHs26Gd6+BzUsUlOrAPb3a4uNlJnvXYdK3/Wp0OSIiUk0KSA1NcCu4/iV45Ce4fAR4+cLuTPjgVni7D2z8UkGpFjUN9OGO7pGAsy+SiIjUDwpIDVVQc7g2FR5ZA1eMAu9GsPdH+OgOeKs3rP8c7BqeXhuG926Hl9nEt1sOkr3rsNHliIhINSggNXSB4XDNMzB6LVw5BqwBkLsG5g6BN6+EdZ8qKJ2nVo0bcfNlLQF4Y+kWg6sREZHqUEASJ/8mkDTRGZR6Pw4+QbB/HfznbpjeE9Z+DHa9NqOmHrw6GpMJvvolj015R4wuR0REzkIBSSpqFAp/+D8YvQYSx4FvMBzYAJ/cC9MS4Kc5UKa31J+rmGYBXHdpBADTl6kvkoiIu1NAkjPzawyJY50tSn/4P+ffv26GTx+AqfHw42woKzG6ynplRGIMAJ/9tJecX48aXI2IiPweBST5fb7Bzltuo9c6b8E1CoPftsP/HoIp3SBrJpQWG11lvXBpy2Cubt+UMruDt75RK5KIiDtTQJLq8Ql0duIevdbZqdu/KRzeCZ8/DFO6wqp3odRmdJVu76E+zlak//ywm/0Fxw2uRkREqqKAJOfG6u98LMAja+Da5yEgAvJ3wRcp8FocZMyAEv3wV6VH21C6RzWmuMzOO99uN7ocERGpggKS1Iy1EVz+F+cDJ6//BwS1hCN74cvH4bUukP4GFKufzZmMONGKNPv7nRw+qtuTIiLuSAFJzo+3L/QY7nzX2w2vQnAkFObConHwWmdY+TrYCo2u0q0ktm9Kx+ZBHC0u4/3vdhhdjoiInIFbBKRp06YRFRWFr68vCQkJZGZmVrntvHnziI+PJyQkBH9/f+Li4pg1a5ZrfUlJCU8++SSdOnXC39+fFi1aMHToUPbu3VvhOFFRUZhMpgrT888/X2fX6PG8fJwvwx212vly3JA2UHQAFv/NGZRWvAI2Pf8HwGQyufoi/XPlDgptemyCiIi7MTwgzZ07l5SUFCZMmMDq1avp0qUL/fv3Z//+/WfcPjQ0lPHjx5Oens6aNWtITk4mOTmZRYsWAXD06FFWr17N3/72N1avXs28efPYuHEjN91002nHevrpp9m3b59rGjVqVJ1ea4PgZYWuQ2FUFtw8HULbwdFfIe3vMLkTLH8JjucbXaXhrr00gnZN/Mk/VsJHGTlGlyMiIpWYHA5j30yakJBA9+7dmTp1KgB2u53IyEhGjRrF2LFjq3WMrl27MmDAACZNmnTG9atWraJHjx7s3LmT1q1bA84WpNGjRzN69OhqncNms2GznRylVVBQQGRkJPn5+QQFBVXrGA1SWSmsmwffvAQHNzmX+QQ7+y9d/qDz+UoN1L9/2MUTH6+hWaAP3zzRB19vi9EliYh4vIKCAoKDg8/6+21oC1JxcTFZWVkkJSW5lpnNZpKSkkhPTz/r/g6Hg7S0NDZu3Ejv3r2r3C4/Px+TyURISEiF5c8//zxhYWFcdtllvPTSS5SWVn2rIzU1leDgYNcUGRl59gsUsHhB59thxPcw8D1o2gFs+bD8eXi1E6RNgqOHjK7SEDfHtaRFsC/7j9j4ZPVuo8sREZFTGBqQDh48SFlZGeHh4RWWh4eHk5ubW+V++fn5BAQEYLVaGTBgAFOmTKFfv35n3Pb48eM8+eST3HnnnRWS4sMPP8ycOXNYunQpDzzwAM899xxPPPFEleccN24c+fn5rmnXrl3neLUNnNkCl94Kf/kObv8XhF8KxUdgxT+ct94WT4Cig0ZXeUFZvczc37sdAG8u30ppmV4KLCLiLryMLqAmAgMDyc7OprCwkLS0NFJSUmjXrh2JiYkVtispKeH222/H4XAwffr0CutSUlJc8507d8ZqtfLAAw+QmpqKj4/Paef08fE543I5R2YzdPwjxN4Im76E5S/Avp9g5WTInOHs6H3FwxAYftZDeYJB3Vsz5est7Dp0jPlr9nHzZS2NLklERDC4BalJkyZYLBby8vIqLM/LyyMiIqLK/cxmMzExMcTFxfHoo48ycOBAUlNTK2xTHo527tzJ4sWLz9pPKCEhgdLSUnbs2FHj65FzYDZD7AC4fznc9W9o0RVKjkL6VOeoty/HQsE+o6usc35WC/dc2RaAN5ZtwW43tEugiIicYGhAslqtdOvWjbS0NNcyu91OWloaPXv2rPZx7HZ7hQ7U5eFo8+bNLFmyhLCwsLMeIzs7G7PZTLNmzc7tIuT8mEzQvj8M/xqGfAKtekDpcciY7nzg5BePQb5n988ZcnkbAn282JRXyJL1eWffQURE6pzht9hSUlIYNmwY8fHx9OjRg8mTJ1NUVERycjIAQ4cOpWXLlq4WotTUVOLj44mOjsZms7FgwQJmzZrluoVWUlLCwIEDWb16NfPnz6esrMzVnyk0NBSr1Up6ejoZGRn06dOHwMBA0tPTGTNmDEOGDKFx44Y7qspQJhPEJEF0X9i+HJa9ADnfwaq3Iet9uGyI811wjdsYXWmtC/bz5s892/DGsq1MW7aVfh3DMZlMRpclItKgGR6QBg0axIEDB3jqqafIzc0lLi6OhQsXujpu5+TkYDafbOgqKipixIgR7N69Gz8/P2JjY5k9ezaDBg0CYM+ePXz22WcAxMXFVTjX0qVLSUxMxMfHhzlz5jBx4kRsNhtt27ZlzJgxFfoliUFMJmiX6Jy2r3D2UdqxArL+CT/Ogi53wlUpzucreZB7rmzLu99u56ddh/lu66/0imlidEkiIg2a4c9Bqq+q+xwFqQU7v4PlL8K2pc6/TRboPAiuehSaxBhbWy2a+Nk63v9uB1dEh/Hh8MuNLkdExCPVi+cgiVRLmytg6H/h3sUQ0w8cZfDThzCtO3wyHI4dNrrCWjG8dzu8zCa+2/orq3N+M7ocEZEGTQFJ6o/IHjDkY2eH7vbXgcMOa//tfN+bB2gZ4sctJ4b5v7F0q8HViIg0bApIUv+07AZ3zYE75zr/XvMfj3ka94OJ0ZhMsGR9HhtyC4wuR0SkwVJAkvqrfX+I6ASlx5wduD1AdNMArr+0OQDTl6kVSUTEKApIUn+ZTNDjAed85jtgLzO2nlryl8RoAD7/aS87fy0yuBoRkYZJAUnqt04DwS8U8nNg45dGV1MrLm0ZTOLFTbE74M3l24wuR0SkQVJAkvrN2w+6DXPOZ75lbC216KE+zscXfJK1m9z84wZXIyLS8CggSf0Xfy+YzLD9G8j7xehqakX3qFB6RIVSXGbnnRVqRRIRudBqFJBmzpzJF1984fr7iSeeICQkhCuuuIKdO3fWWnEi1RIS6XzxLUDmDGNrqUUj+jj7In2QkcNvRcUGVyMi0rDUKCA999xz+Pn5AZCens60adN48cUXadKkCWPGjKnVAkWqpbyz9pq5cMwzHrJ4dfumXNIiiGMlZfzzux1GlyMi0qDUKCDt2rWLmBhnH4n//ve/3Hrrrdx///2kpqayYsWKWi1QpFqiroRml0DJUfjxA6OrqRUmk8nVF+n9ldsptJUaXJGISMNRo4AUEBDAr7/+CsBXX31Fv379APD19eXYsWO1V51IdZlMkHC/cz5zhscM+e9/SQTtmvpTcLyUD77X7WsRkQulRgGpX79+3Hfffdx3331s2rSJ66+/HoB169YRFRVVm/WJVF+n28E3BA7vhM1fGV1NrbCYTfzlamdfpLdXbOd4iWcEPxERd1ejgDRt2jR69uzJgQMH+OSTTwgLCwMgKyuLO++8s1YLFKk2ayPoOtQ5n+E5Q/5vvqwlLUP8OFho4z9Zu40uR0SkQTA5HA6H0UXURwUFBQQHB5Ofn09QUJDR5Ui533bC63HOF9k+lAlNLza6olox87sdTPhsHa0a+7H0sUS8LXpCh4hITVT397tG/5VduHAh3377revvadOmERcXx1133cVvv3nGCCKppxq3gfbXOec9aMj/oO6RNAmwsvu3Y3z+016jyxER8Xg1CkiPP/44BQXON42vXbuWRx99lOuvv57t27eTkpJSqwWKnLPyztrZH8HxfGNrqSW+3hbuubItAG8s24rdroZfEZG6VKOAtH37djp27AjAJ598wg033MBzzz3HtGnT+PJLz3gfltRjba+GprFQUuQxQ/4BhlzehkBfL7bsL+SrX/KMLkdExKPVKCBZrVaOHj0KwJIlS7jmmmsACA0NdbUsiRjGZIIeJ1qRVr0Ndrux9dSSIF9vhvWMAuCNZVtQ90ERkbpTo4B05ZVXkpKSwqRJk8jMzGTAAOdrHjZt2kSrVq1qtUCRGuk8CHyC4dA22LLE6GpqTXKvKHy9zazZnc+3Ww4aXY6IiMeqUUCaOnUqXl5efPzxx0yfPp2WLVsC8OWXX3LttdfWaoEiNeITAF3/7JzP9Jwh/2EBPtzZozUA05ZuMbgaERHPpWH+NaRh/vXAoe3w+mWAA0b+AE0uMrqiWrH38DGufmkpJWUOPvlLT7q1CTW6JBGReqNOh/kDlJWV8cknn/DMM8/wzDPP8Omnn1JWpqf8ihsJbQvt+zvnM982tpZa1CLEjz9d5ryV/cbSrQZXIyLimWoUkLZs2UKHDh0YOnQo8+bNY968eQwZMoRLLrmErVv1H2xxIwkPOD+zP4TjnjOA4MHEaMwmSNuwn1/2es51iYi4ixoFpIcffpjo6Gh27drF6tWrWb16NTk5ObRt25aHH364tmsUqbl2faBJeyg+Aj99ZHQ1taZtE3+u79QcgOnL9Y8SEZHaVqOAtHz5cl588UVCQ0/2fQgLC+P5559n+fLltVacyHk7dch/5gyPGfIPMCIxBoAv1uxlx8Eig6sREfEsNQpIPj4+HDly5LTlhYWFWK3W8y5KpFZ1uQOsgfDrFtj2tdHV1JqOLYL4Q2wz7A546xu1IomI1KYaBaQbbriB+++/n4yMDBwOBw6Hg++//54HH3yQm266qbZrFDk/PoFw2RDnfIbnvJ8N4KE+0QB8nLWb3PzjBlcjIuI5ahSQXn/9daKjo+nZsye+vr74+vpyxRVXEBMTw+TJk2u5RJFa0GO483PzV/Cr57S2dGsTSkLbUErKHLy9YpvR5YiIeIwaBaSQkBD+97//sWnTJj7++GM+/vhjNm3axKeffkpISEgtlyhSC8KiIaYf4IBV7xhdTa16qI+zL9KHGTkcKio2uBoREc/gVd0NU1JSfnf90qVLXfOvvPJKzSsSqSsJD8KWxfDjbOgz3vm0bQ9w1UVN6NQymLV78nl/5XZSrrnY6JJEROq9agekH3/8sVrbmUymGhcjUqei/wCh0XBoq3PIf/ltt3rOZDLxUJ9oHpy9mve/28Hw3u0I9PU2uiwRkXqt2gHp1BYikXrJbHYO+V/4pPPJ2t3vcz4GwANc0zGC6Kb+bD1QxAcZOTx4dbTRJYmI1Gs1ftWISL0UdxdYA+DgRti2zOhqao3ZbHI9F+mdFds5XqLX/oiInA8FJGlYfIOcIQkg4y1ja6llN8W1oGWIHwcLbfznh11GlyMiUq8pIEnDU/5k7U0L4dB2Y2upRd4WMw9e3Q6AN5dvo6TMc54aLiJyoSkgScPT5CJnh20PHPJ/W3wkTQJ82HP4GJ9l7zW6HBGResstAtK0adOIiorC19eXhIQEMjMzq9x23rx5xMfHExISgr+/P3FxccyaNcu1vqSkhCeffJJOnTrh7+9PixYtGDp0KHv3VvyxOHToEIMHDyYoKIiQkBDuvfdeCgsL6+waxc0kPOj8/HEWFHvOe8x8vS3cd1VbAN5YtgW73WFwRSIi9ZPhAWnu3LmkpKQwYcIEVq9eTZcuXejfvz/79+8/4/ahoaGMHz+e9PR01qxZQ3JyMsnJySxatAiAo0ePsnr1av72t7+xevVq5s2bx8aNG097BcrgwYNZt24dixcvZv78+XzzzTfcf//9dX694iZi+kHjtnA8H9bMNbqaWjU4oTVBvl5sPVDEV7/kGl2OiEi9ZHI4HIb+EzMhIYHu3bszdepUAOx2O5GRkYwaNYqxY8dW6xhdu3ZlwIABTJo06YzrV61aRY8ePdi5cyetW7dm/fr1dOzYkVWrVhEfHw/AwoULuf7669m9ezctWrQ46zkLCgoIDg4mPz+foKCgal6tuJX0abDor9C0A4xI95gh/wCvfLWR17/eQqeWwXw2speeTyYickJ1f78NbUEqLi4mKyuLpKQk1zKz2UxSUhLp6eln3d/hcJCWlsbGjRvp3bt3ldvl5+djMplcr0FJT08nJCTEFY4AkpKSMJvNZGRknPEYNpuNgoKCCpPUc3GDwdsfDqyHHSuMrqZW3d2rLX7eFtbuyWfF5oNGlyMiUu8YGpAOHjxIWVkZ4eHhFZaHh4eTm1v1rYH8/HwCAgKwWq0MGDCAKVOm0K9fvzNue/z4cZ588knuvPNOV1LMzc2lWbNmFbbz8vIiNDS0yvOmpqYSHBzsmiIjI8/lUsUd+YVAlzuc8x425D/U38pdCa0BmLZ0i8HViIjUP4b3QaqJwMBAsrOzWbVqFc8++ywpKSksW7bstO1KSkq4/fbbcTgcTJ8+/bzOOW7cOPLz813Trl16zoxHKB/yv3EBHM4xtpZaNvyqdnhbTGRsP8QPOw4ZXY6ISL1iaEBq0qQJFouFvLy8Csvz8vKIiIiocj+z2UxMTAxxcXE8+uijDBw4kNTU1ArblIejnTt3snjx4gr3GSMiIk7rBF5aWsqhQ4eqPK+Pjw9BQUEVJvEAzWKhXSI47B435D8i2JeB3VoB8MayrQZXIyJSv1T7XWx1wWq10q1bN9LS0rj55psBZyfttLQ0Ro4cWe3j2O12bDab6+/ycLR582aWLl1KWFhYhe179uzJ4cOHycrKolu3bgB8/fXX2O12EhISzv/CpH7p8YDztSNZM+HqsWBtZHRFteaB3tHMXbWLrzfsZ93efC5pEWx0SbWqzO7geEkZtlI7x0vKTkx2jpeWYXN9nlhWYTvnulOX2U5sU76v2WTCy2LC22LG22LG6nVy3jmdsq583suMl9mE1avidlaLGa9T5r29Tj+Ga92px7GYMZvVwV7ECIYGJICUlBSGDRtGfHw8PXr0YPLkyRQVFZGcnAzA0KFDadmypauFKDU1lfj4eKKjo7HZbCxYsIBZs2a5bqGVlJQwcOBAVq9ezfz58ykrK3P1KwoNDcVqtdKhQweuvfZahg8fzptvvklJSQkjR47kjjvuqNYINvEw7ftDSGvnLba1/4Fuw4yuqNZENfHnhs4t+OynvUxftpWpd3Wts3OVhxVnyDgldJQHkdNCysntjpfYsZWe+DwRUk5ddmr4sZWe3KakzPOf82Qxm1xByhm0Ts47w5SpitB28m+vSiGuPNR5VQ545evNp257Mrh5VQpx5ev8rBZ8vCxGf1UitcrwgDRo0CAOHDjAU089RW5uLnFxcSxcuNDVcTsnJwez+eSdwKKiIkaMGMHu3bvx8/MjNjaW2bNnM2jQIAD27NnDZ599BkBcXFyFcy1dupTExEQAPvjgA0aOHEnfvn0xm83ceuutvP7663V/weJ+zBboPhwW/w0yZ0DXoR415P8vidF89tNevli7j5t/yaOR1VKhheVkAKkYSGy/G1JOX1dq8EMpvS0mfL0s+Hhb8PU24+ttwcfL+enrbcbXy+Jaduo2zn3M+Lq2dW7jAErK7JSUOU582ikuPfl3aZmd4lPWOdc7KLWfnD91XUmFbR0njmWn1O6gpNROcZlzqvzglTK740T4dO9XxzSyWgjx8ya4kdX56edNSCNvght5E+JnJaSR94n1p/zdyBs/b4seQyFuyfDnINVXeg6Shzn2G7zSEUqOwt0LIKqX0RXVqvtmrmLJ+jM/fLUuWC1mZ+g4EUR8vCqGlPJlPqeElArbVQo5PqdsUzH4nNzP4iG3osrsjtNCVXmYqhyySk6EqpLSE0GrUog7LeCV2Sk5EdxK7aeHuOIyB6WnzJeU/k7AOzF/vr8gVov5RGg6Eaj8vAk+JVA5Q5bVNR/iZyW4kTeBPl66/Sg1Ut3fb8NbkETcgl9j6Hw7ZL0PGW96XEB6vH8sO389yvHSsgoh5ewBpGIrS8UAc+YWGquX2WPCihEsZhMWs/O7rA9Ky+wU2co4fKyY/GMlHD5awuFjJeQfLXbNHz5aQv6x0/8uD1oHjtg4cMR29pOdwmziRCuVlSA/71MCVKVA1ahi4Ar288bLUi8HcMsFphakGlILkgfKWwfTrwCTBUavgeBWRlck4rEcDgdHi8tOCVXF5J8SoFx/n5h3hirn38dKys7r3IE+XgQ3OnkbsLxVqnIrVciJAFbeslVfQqv8PrUgiZyr8Esg6irnU7VXvQtJE4yuSMRjmUwm/H288PfxokWI3zntayt1BqsKgepocYWwdWqgKg9bBcdLAThiK+WIrZTdvx07p/P6epudoapyoGpkrRC2Qk4NX42s+FvVz6o+UkASOVXCA86AlPU+XP0EeJ/bf7hFpO75eFloFmihWaDvOe1XWmbnyPHSE6Gq+MStwJPzJ0PVibB1Sggr7yh/vMRGXsG53Q70MpucfasaedPIasGECZPJGRJNOMeEOD9P/du58NS/nfucnOe0fSr+Tfk+gNlUcX/OeM6Tf3PqOc9QA1WsM58ozHSm/atz7BM7ly+/5bKWtAnzP6fvu7YoIImcqv11EBwJ+bvg50/gsiFGVyQitcTLYqaxv5XG/lag+j+6DoeDQlvpaa1SpwYq17wrVBXz29ESik90oP+1qJhfi4rr7uI8VNfWjRWQRNyCxQu63wtLJjrfzxY32KOG/IvIuTOZTAT6ehPo6825voXzeElZhUB1rKQMHGB3OHA4wIEzgDk/AU5dDg4qbkfl5ZWOwWn7nDjXiR1dxz11vqpjV/qb8vNUUV/58e1n2IZT6/y9Y1da3iLk3FoJa5MCkkhlXYfBsuchdw3syoDWlxtdkYjUU77eFiKCLUQEG/dDLzWjsY4ilTUKhU63Oecz3jS2FhERMYQCksiZJDzg/PzlMyjYa2wtIiJywSkgiZxJRCdo0wscZfDDe0ZXIyIiF5gCkkhVetzv/Pzhn1By3NhaRETkglJAEqlK7A0Q1BKOHoR1nxpdjYiIXEAKSCJVKR/yD87O2norj4hIg6GAJPJ7ut4NFh/Ylw27VxldjYiIXCAKSCK/xz8MOg10zme8ZWwtIiJywSggiZxNeWftX/4LR3INLUVERC4MBSSRs2kRB5GXg73UOaJNREQ8ngKSSHUklA/5fw9K9cJJERFPp4AkUh0dboLA5lC033mrTUREPJoCkkh1WLwhvnzIvzpri4h4OgUkkerqdjdYrLDnB9idZXQ1IiJShxSQRKoroClc8ifnfKZakUREPJkCksi5KO+s/fM8KNxvbC0iIlJnFJBEzkXLbtCqO9hLIOt9o6sREZE6ooAkcq56POD8XPWuhvyLiHgoBSSRc9XxjxAQDoW5sP4zo6sREZE6oIAkcq68rBB/j3M+c4axtYiISJ1QQBKpiW7JYPaGXRmw90ejqxERkVqmgCRSE4HhcMnNzvkMtSKJiHgaBSSRmkp40Pn588dQeMDYWkREpFYpIInUVKt4aNEVyoph9ftGVyMiIrVIAUnkfCSUD/l/D8pKjK1FRERqjQKSyPm45BbwbwpH9sKG+UZXIyIitUQBSeR8ePk4R7SBOmuLiHgQBSSR8xV/D5i9IOc72LfG6GpERKQWKCCJnK+g5tDhJud85lvG1iIiIrVCAUmkNpQP+V/7MRT9amwtIiJy3gwPSNOmTSMqKgpfX18SEhLIzMysctt58+YRHx9PSEgI/v7+xMXFMWvWrNO2ueaaawgLC8NkMpGdnX3acRITEzGZTBWmBx98sLYvTRqSyB7QvAuUHofVM42uRkREzpOhAWnu3LmkpKQwYcIEVq9eTZcuXejfvz/79+8/4/ahoaGMHz+e9PR01qxZQ3JyMsnJySxatMi1TVFREVdeeSUvvPDC7557+PDh7Nu3zzW9+OKLtXpt0sCYTNCjfMj/u1BWamw9IiJyXkwOh8Nh1MkTEhLo3r07U6dOBcButxMZGcmoUaMYO3ZstY7RtWtXBgwYwKRJkyos37FjB23btuXHH38kLi6uwrrExETi4uKYPHlyjWsvKCggODiY/Px8goKCanwc8SAlx+HVjnD0V7h9FnS8yeiKRESkkur+fhvWglRcXExWVhZJSUknizGbSUpKIj09/az7OxwO0tLS2LhxI7179z7n83/wwQc0adKESy+9lHHjxnH06NHf3d5ms1FQUFBhEqnA2xe63e2cz9SQfxGR+szLqBMfPHiQsrIywsPDKywPDw9nw4YNVe6Xn59Py5YtsdlsWCwW3njjDfr163dO577rrrto06YNLVq0YM2aNTz55JNs3LiRefPmVblPamoqf//738/pPNIAxd8L306GHSsg92eIuNToikREpAYMC0g1FRgYSHZ2NoWFhaSlpZGSkkK7du1ITEys9jHuv/9+13ynTp1o3rw5ffv2ZevWrURHR59xn3HjxpGSkuL6u6CggMjIyBpfh3io4JbQ4Qb45X/OVqSbXje6IhERqQHDAlKTJk2wWCzk5eVVWJ6Xl0dERESV+5nNZmJiYgCIi4tj/fr1pKamnlNAqiwhIQGALVu2VBmQfHx88PHxqfE5pAFJeNAZkNb8G5ImQqNQoysSEZFzZFgfJKvVSrdu3UhLS3Mts9vtpKWl0bNnz2ofx263Y7PZzquW8kcBNG/e/LyOIwJA654Q3glKj8GPs86+vYiIuB1Db7GlpKQwbNgw4uPj6dGjB5MnT6aoqIjkZOe7rYYOHUrLli1JTU0FnP2A4uPjiY6OxmazsWDBAmbNmsX06dNdxzx06BA5OTns3bsXgI0bNwIQERFBREQEW7du5cMPP+T6668nLCyMNWvWMGbMGHr37k3nzp0v8DcgHslkgoT74bNRsOod6DkSzBajqxIRkXNgaEAaNGgQBw4c4KmnniI3N5e4uDgWLlzo6ridk5OD2XyykauoqIgRI0awe/du/Pz8iI2NZfbs2QwaNMi1zWeffeYKWAB33HEHABMmTGDixIlYrVaWLFniCmORkZHceuut/N///d8FumppEDrdBoufgsM5sGkhxA4wuiIRETkHhj4HqT7Tc5DkrBZPgJWToW1vGPa50dWIiAj14DlIIh6v+71gMsP2b2D/eqOrERGRc6CAJFJXQlqfvLWmB0eKiNQrCkgidan8/Ww/zYFjhw0tRUREqk8BSaQuRV0JzTpCyVH4cbbR1YiISDUpIInUJZMJepx4cvuqt8FeZmw9IiJSLQpIInWt8+3gGwK/7YDNi42uRkREqkEBSaSuWf2h65+d8xlvGluLiIhUiwKSyIXQ/T7ABNuWwoFNRlcjIiJnoYAkciE0joKLr3fOa8i/iIjbU0ASuVASTnTW/ukjOJ5vbC0iIvK7FJBELpS2V0PTWCguhOwPja5GRER+hwKSyIVy6pD/zBlgtxtbj4iIVEkBSeRC6jwIfILh0DbYmmZ0NSIiUgUFJJELyScALhvinNeQfxERt6WAJHKh9Tgx5H/LEji4xehqRETkDBSQRC600HbQvr9zftXbxtYiIiJnpIAkYoTyzto/fgC2I8bWIiIip1FAEjFCuz4QdhEUH4Hsj4yuRkREKlFAEjGC2QwJDzjnNeRfRMTtKCCJGKXLHWANhF83O9/RJiIibkMBScQoPoFw2WDnfMZbxtYiIiIVKCCJGKm8s/bmr5wPjxQREbeggCRipLBoiOkHOCDzHaOrERGRExSQRIxW3ln7x1lgKzS2FhERARSQRIwX3RdCo8FWAGvmGF2NiIiggCRiPLP5ZF+kjBngcBhbj4iIKCCJuIW4u8AaAAc3wrZlRlcjItLgKSCJuAPfIOhyp3M+c4axtYiIiAKSiNsov8228Uv4bYehpYiINHQKSCLuoml7iP4D4IBVGvIvImIkBSQRd9LjxJD/1f+C4iJjaxERacAUkETcyUX9oHEUHM+HNf82uhoRkQZLAUnEnZgtJ/siZWrIv4iIURSQRNxN3GDwbgT7f4EdK4yuRkSkQVJAEnE3fiHQ5Q7nfMZbhpYiItJQKSCJuKPyztobF8DhHGNrERFpgBSQRNxRs1hoezU47LDqXaOrERFpcBSQRNxVQvmQ/5lQcszYWkREGhjDA9K0adOIiorC19eXhIQEMjMzq9x23rx5xMfHExISgr+/P3FxccyaNeu0ba655hrCwsIwmUxkZ2efdpzjx4/z0EMPERYWRkBAALfeeit5eXm1fWki56f9tRDSGo79Bmv/Y3Q1IiINiqEBae7cuaSkpDBhwgRWr15Nly5d6N+/P/v37z/j9qGhoYwfP5709HTWrFlDcnIyycnJLFq0yLVNUVERV155JS+88EKV5x0zZgyff/45//nPf1i+fDl79+7lT3/6U61fn8h5MVug+3DnfIaG/IuIXEgmh8O4/+omJCTQvXt3pk6dCoDdbicyMpJRo0YxduzYah2ja9euDBgwgEmTJlVYvmPHDtq2bcuPP/5IXFyca3l+fj5Nmzblww8/ZODAgQBs2LCBDh06kJ6ezuWXX37G89hsNmw2m+vvgoICIiMjyc/PJygo6FwuW6T6jh6CVzpC6TG4ewFE9TK6IhGReq2goIDg4OCz/n4b1oJUXFxMVlYWSUlJJ4sxm0lKSiI9Pf2s+zscDtLS0ti4cSO9e/eu9nmzsrIoKSmpcN7Y2Fhat279u+dNTU0lODjYNUVGRlb7nCI11igUOt/unM/UkH8RkQvFsIB08OBBysrKCA8Pr7A8PDyc3NzcKvfLz88nICAAq9XKgAEDmDJlCv369av2eXNzc7FarYSEhJzTeceNG0d+fr5r2rVrV7XPKXJeyjtrr58P+buNrUVEpIHwMrqAcxUYGEh2djaFhYWkpaWRkpJCu3btSExMrNPz+vj44OPjU6fnEDmj8Esg6irnU7VXvQtJE4yuSETE4xnWgtSkSRMsFstpo8fy8vKIiIiocj+z2UxMTAxxcXE8+uijDBw4kNTU1GqfNyIiguLiYg4fPnxO5xUxVPn72VbPhJLjxtYiItIAGBaQrFYr3bp1Iy0tzbXMbreTlpZGz549q30cu91eofP02XTr1g1vb+8K5924cSM5OTnndF6RC+ri6yE4Eo7+Cj9/YnQ1IiIez9BbbCkpKQwbNoz4+Hh69OjB5MmTKSoqIjk5GYChQ4fSsmVLVwtRamoq8fHxREdHY7PZWLBgAbNmzWL69OmuYx46dIicnBz27t0LOMMPOFuOIiIiCA4O5t577yUlJYXQ0FCCgoIYNWoUPXv2rHIEm4jhLF7Q/V5YMhEy3oS4u8BkMroqERGPZWhAGjRoEAcOHOCpp54iNzeXuLg4Fi5c6Oq4nZOTg9l8spGrqKiIESNGsHv3bvz8/IiNjWX27NkMGjTItc1nn33mClgAd9zhfOnnhAkTmDhxIgCvvvoqZrOZW2+9FZvNRv/+/XnjjTcuwBWLnIeuw2DZ85C7BnZlQGsFehGRumLoc5Dqs+o+R0GkVv1vJPw4Cy75E9z2T6OrERGpd9z+OUgiUgOuIf+fQcFeY2sREfFgCkgi9UlEJ2h9BdhL4Yf3jK5GRMRjKSCJ1DcJJ4b8Z70PpdUfwSkiItWngCRS38TeAEEtoegArPvU6GpERDySApJIfWPxhvh7nPMZb4LGWYiI1DoFJJH6qNvdYPGBvT/C7h+MrkZExOMoIInUR/5NoNNA53zmW8bWIiLigRSQROqr8vezrfsvHMk1tBQREU+jgCRSX7WIg8gEsJfAD3popIhIbVJAEqnPyluRsv4JpcXG1iIi4kEUkETqs45/hMDmUJgHv/zP6GpERDyGApJIfVZ5yL+IiNQKBSSR+q7b3WCxwp4fYE+W0dWIiHgEBSSR+i6gGVzyJ+d8xgxjaxER8RAKSCKeoPz9bOvmQeF+Y2sREfEACkginqBlN2gZD2XFzpfYiojIeVFAEvEUCQ86P394D8pKjK1FRKSeU0AS8RQd/wgB4XBkH6z/zOhqRETqNQUkEU/hZYVuyc75DL2fTUTkfCggiXiS+GQwe8GuDNibbXQ1IiL1lgKSiCcJjIBLbnHOZ2rIv4hITSkgiXiaHg84P9d+DEUHja1FRKSeUkAS8TSt4qHFZVBm05B/EZEaUkAS8TQmU6Uh/6XG1iMiUg8pIIl4oktuAf+mULAHNsw3uhoRkXpHAUnEE3n5OF9iCxryLyJSAwpIIp4q/h7nkP+c7yB3rdHViIjUKwpIIp4qqAV0uMk5r1YkEZFzooAk4skSyof8/weOHjK2FhGRekQBScSTRSZARGcoPQ6rZxpdjYhIvaGAJOLJTh3yv+pdDfkXEakmL6MLEJE6dumtsPhvkL8LFjwKIW3AYnVOXtaT8xZvsPic+Py99daK25j17ywR8TwKSCKeztvXOeR/xct182Rts1fVAepsAev31nudIYyduu1p66sId2ZvhTgROWcKSCINwZVjABMcOwSlxVBWPpU4X0nimi8++3p7pdt09lLnVGLIlVWP2et3WseqCFjevuDtB96NwMvX+entd8p04u8K6xqd2K98nZ/CmUg9pYAk0hD4BELfv9XOsexlJ8PSGQOW7ZT1lcPW760/sc35rC8Pd46ySjUbGOIsPmcOT+UBqlrr/CpOZ1pn8Tbg4kQ8lwKSiJwbs8U5efsaXUnVXCGuUpiqduuZzRnESo5CyfETn8ecU+mJT9eyU9aXrys9frKWMptzOn64bq/Z7FVFq1alVq9qr6vcGnZi3svH2flfxMMpIImI5zE6xNntJ8LSqeHqqDM4nRq2So5Vsa48mFVjPxwnzlkKtgLnVKdMVYSnan56+Tr39fKr4vMM+yiQiQHcIiBNmzaNl156idzcXLp06cKUKVPo0aPHGbedN28ezz33HFu2bKGkpISLLrqIRx99lD//+c+ubRwOBxMmTODtt9/m8OHD9OrVi+nTp3PRRRe5tomKimLnzp0Vjp2amsrYsWPr5iJFpOEwm8Hq75wIq7vzOBzOlq7SysHp2OmtWr/X4nWmda5gdmLe1ffMASVFzolf6+7aTuVVRXA67fP3gtcZPr18znws3a4U3CAgzZ07l5SUFN58800SEhKYPHky/fv3Z+PGjTRr1uy07UNDQxk/fjyxsbFYrVbmz59PcnIyzZo1o3///gC8+OKLvP7668ycOZO2bdvyt7/9jf79+/PLL7/g63vyX5RPP/00w4cPd/0dGBhY9xcsIlJbTKYTLTm+4Ne4bs9VVvI7LV7HT7aY1fjz+MnbkyXHKvYjKz2xvq5vU5YzWc4/fFX1afECKrWIndZCdrb11dnmHNfXuI7zPcZZ6vBv6vy/gQFMDofDYciZT0hISKB79+5MnToVALvdTmRkJKNGjap2a07Xrl0ZMGAAkyZNwuFw0KJFCx599FEee+wxAPLz8wkPD+f999/njjvuAJwtSKNHj2b06NE1qrugoIDg4GDy8/MJCgqq0TFERKQK5YGscnByfdpqHr7O9HlqvzFxH0PmQUzfWj1kdX+/DW1BKi4uJisri3HjxrmWmc1mkpKSSE9PP+v+DoeDr7/+mo0bN/LCCy8AsH37dnJzc0lKSnJtFxwcTEJCAunp6a6ABPD8888zadIkWrduzV133cWYMWPw8jrzV2Kz2bDZbK6/Cwrq+j6/iEgDZvE+cavrAv0D1OGoIkBVClrVCVtVfh4He8np56244NzWX7BjnLbDhanDZNxjMgwNSAcPHqSsrIzw8PAKy8PDw9mwYUOV++Xn59OyZUtsNhsWi4U33niDfv36AZCbm+s6RuVjlq8DePjhh+natSuhoaF89913jBs3jn379vHKK6+c8Zypqan8/e9/r9F1ioiImzOZTo7oE8EN+iDVRGBgINnZ2RQWFpKWlkZKSgrt2rUjMTGx2sdISUlxzXfu3Bmr1coDDzxAamoqPj4+p20/bty4CvsUFBQQGRl5XtchIiIi7snQgNSkSRMsFgt5eXkVlufl5REREVHlfmazmZiYGADi4uJYv349qampJCYmuvbLy8ujefPmFY4ZFxdX5TETEhIoLS1lx44dXHzxxaet9/HxOWNwEhEREc9j6DPwrVYr3bp1Iy0tzbXMbreTlpZGz549q30cu93u6h/Utm1bIiIiKhyzoKCAjIyM3z1mdnY2ZrP5jCPnREREpGEx/BZbSkoKw4YNIz4+nh49ejB58mSKiopITk4GYOjQobRs2ZLU1FTA2RcoPj6e6OhobDYbCxYsYNasWUyfPh0Ak8nE6NGjeeaZZ7joootcw/xbtGjBzTffDEB6ejoZGRn06dOHwMBA0tPTGTNmDEOGDKFx4zoeKisiIiJuz/CANGjQIA4cOMBTTz1Fbm4ucXFxLFy40NXJOicnB/MpL3ssKipixIgR7N69Gz8/P2JjY5k9ezaDBg1ybfPEE09QVFTE/fffz+HDh7nyyitZuHCh6xlIPj4+zJkzh4kTJ2Kz2Wjbti1jxoyp0MdIREREGi7Dn4NUX+k5SCIiIvVPdX+/De2DJCIiIuKOFJBEREREKlFAEhEREalEAUlERESkEgUkERERkUoUkEREREQqUUASERERqUQBSURERKQSw5+kXV+VP1+zoKDA4EpERESkusp/t8/2nGwFpBo6cuQIAJGRkQZXIiIiIufqyJEjBAcHV7lerxqpIbvdzt69ewkMDMRkMtXacQsKCoiMjGTXrl16hclZ6Ls6N/q+qk/fVfXpu6o+fVfVV5fflcPh4MiRI7Ro0aLCu14rUwtSDZnNZlq1alVnxw8KCtL/A1WTvqtzo++r+vRdVZ++q+rTd1V9dfVd/V7LUTl10hYRERGpRAFJREREpBIFJDfj4+PDhAkT8PHxMboUt6fv6tzo+6o+fVfVp++q+vRdVZ87fFfqpC0iIiJSiVqQRERERCpRQBIRERGpRAFJREREpBIFJBEREZFKFJDczLRp04iKisLX15eEhAQyMzONLsktffPNN9x44420aNECk8nEf//7X6NLckupqal0796dwMBAmjVrxs0338zGjRuNLsstTZ8+nc6dO7seTNezZ0++/PJLo8uqF55//nlMJhOjR482uhS3NHHiREwmU4UpNjbW6LLc1p49exgyZAhhYWH4+fnRqVMnfvjhhwtehwKSG5k7dy4pKSlMmDCB1atX06VLF/r378/+/fuNLs3tFBUV0aVLF6ZNm2Z0KW5t+fLlPPTQQ3z//fcsXryYkpISrrnmGoqKiowuze20atWK559/nqysLH744Qf+8Ic/8Mc//pF169YZXZpbW7VqFW+99RadO3c2uhS3dskll7Bv3z7X9O233xpdklv67bff6NWrF97e3nz55Zf88ssvvPzyyzRu3PiC16Jh/m4kISGB7t27M3XqVMD5vrfIyEhGjRrF2LFjDa7OfZlMJj799FNuvvlmo0txewcOHKBZs2YsX76c3r17G12O2wsNDeWll17i3nvvNboUt1RYWEjXrl154403eOaZZ4iLi2Py5MlGl+V2Jk6cyH//+1+ys7ONLsXtjR07lpUrV7JixQqjS1ELkrsoLi4mKyuLpKQk1zKz2UxSUhLp6ekGViaeJD8/H3D+8EvVysrKmDNnDkVFRfTs2dPoctzWQw89xIABAyr8d0vObPPmzbRo0YJ27doxePBgcnJyjC7JLX322WfEx8dz22230axZMy677DLefvttQ2pRQHITBw8epKysjPDw8ArLw8PDyc3NNagq8SR2u53Ro0fTq1cvLr30UqPLcUtr164lICAAHx8fHnzwQT799FM6duxodFluac6cOaxevZrU1FSjS3F7CQkJvP/++yxcuJDp06ezfft2rrrqKo4cOWJ0aW5n27ZtTJ8+nYsuuohFixbxl7/8hYcffpiZM2de8Fq8LvgZRcQQDz30ED///LP6PvyOiy++mOzsbPLz8/n4448ZNmwYy5cvV0iqZNeuXTzyyCMsXrwYX19fo8txe9ddd51rvnPnziQkJNCmTRv+/e9/6/ZtJXa7nfj4eJ577jkALrvsMn7++WfefPNNhg0bdkFrUQuSm2jSpAkWi4W8vLwKy/Py8oiIiDCoKvEUI0eOZP78+SxdupRWrVoZXY7bslqtxMTE0K1bN1JTU+nSpQuvvfaa0WW5naysLPbv30/Xrl3x8vLCy8uL5cuX8/rrr+Pl5UVZWZnRJbq1kJAQ2rdvz5YtW4wuxe00b978tH+QdOjQwZBbkgpIbsJqtdKtWzfS0tJcy+x2O2lpaeoDITXmcDgYOXIkn376KV9//TVt27Y1uqR6xW63Y7PZjC7D7fTt25e1a9eSnZ3tmuLj4xk8eDDZ2dlYLBajS3RrhYWFbN26lebNmxtditvp1avXaY8i2bRpE23atLngtegWmxtJSUlh2LBhxMfH06NHDyZPnkxRURHJyclGl+Z2CgsLK/zra/v27WRnZxMaGkrr1q0NrMy9PPTQQ3z44Yf873//IzAw0NWfLTg4GD8/P4Orcy/jxo3juuuuo3Xr1hw5coQPP/yQZcuWsWjRIqNLczuBgYGn9WPz9/cnLCxM/dvO4LHHHuPGG2+kTZs27N27lwkTJmCxWLjzzjuNLs3tjBkzhiuuuILnnnuO22+/nczMTGbMmMGMGTMufDEOcStTpkxxtG7d2mG1Wh09evRwfP/990aX5JaWLl3qAE6bhg0bZnRpbuVM3xHg+Oc//2l0aW7nnnvucbRp08ZhtVodTZs2dfTt29fx1VdfGV1WvXH11Vc7HnnkEaPLcEuDBg1yNG/e3GG1Wh0tW7Z0DBo0yLFlyxajy3Jbn3/+uePSSy91+Pj4OGJjYx0zZswwpA49B0lERESkEvVBEhEREalEAUlERESkEgUkERERkUoUkEREREQqUUASERERqUQBSURERKQSBSQRERGRShSQRERERCpRQBIRqQXLli3DZDJx+PBho0sRkVqggCQiIiJSiQKSiIiISCUKSCLiEex2O6mpqbRt2xY/Pz+6dOnCxx9/DJy8/fXFF1/QuXNnfH19ufzyy/n5558rHOOTTz7hkksuwcfHh6ioKF5++eUK6202G08++SSRkZH4+PgQExPDu+++W2GbrKws4uPjadSoEVdccQUbN26s2wsXkTqhgCQiHiE1NZV//etfvPnmm6xbt44xY8YwZMgQli9f7trm8ccf5+WXX2bVqlU0bdqUG2+8kZKSEsAZbG6//XbuuOMO1q5dy8SJE/nb3/7G+++/79p/6NChfPTRR7z++uusX7+et956i4CAgAp1jB8/npdffpkffvgBLy8v7rnnngty/SJSu0wOh8NhdBEiIufDZrMRGhrKkiVL6Nmzp2v5fffdx9GjR7n//vvp06cPc+bMYdCgQQAcOnSIVq1a8f7773P77bczePBgDhw4wFdffeXa/4knnuCLL75g3bp1bNq0iYsvvpjFixeTlJR0Wg3Lli2jT58+LFmyhL59+wKwYMECBgwYwLFjx/D19a3jb0FEapNakESk3tuyZQtHjx6lX79+BAQEuKZ//etfbN261bXdqeEpNDSUiy++mPXr1wOwfv16evXqVeG4vXr1YvPmzZSVlZGdnY3FYuHqq6/+3Vo6d+7smm/evDkA+/fvP+9rFJELy8voAkREzldhYSEAX3zxBS1btqywzsfHp0JIqik/P79qbeft7e2aN5lMgLN/lIjUL2pBEpF6r2PHjvj4+JCTk0NMTEyFKTIy0rXd999/75r/7bff2LRpEx06dACgQ4cOrFy5ssJxV65cSfv27bFYLHTq1Am73V6hT5OIeC61IIlIvRcYGMhjjz3GmDFjsNvtXHnlleTn57Ny5UqCgoJo06YNAE8//TRhYWGEh4czfvx4mjRpws033wzAo48+Svfu3Zk0aRKDBg0iPT2dqVOn8sYbbwAQFRXFsGHDuOeee3j99dfp0qULO3fuZP/+/dx+++1GXbqI1BEFJBHxCJMmTaJp06akpqaybds2QkJC6Nq1K3/9619dt7ief/55HnnkETZv3kxcXByff/45VqsVgK5du/Lvf/+bp556ikmTJtG8eXOefvpp7r77btc5pk+fzl//+ldGjBjBr7/+SuvWrfnrX/9qxOWKSB3TKDYR8XjlI8x+++03QkJCjC5HROoB9UESERERqUQBSURERKQS3WITERERqUQtSCIiIiKVKCCJiIiIVKKAJCIiIlKJApKIiIhIJQpIIiIiIpUoIImIiIhUooAkIiIiUokCkoiIiEgl/w/t+JxMI+YCRgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 176384 TEST: 48509\n",
      "dataset built\n",
      "Loss in trainset: 0.3352\n",
      "Loss in testset: 0.3448\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fc9d43210b942e48c23d404d750a059",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/144850 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0837, 2.9252, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71be6641bf6e428bbcd781d81953eaae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/737070 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3304\n",
      "Loss in testset: 0.3435\n",
      "iteration: 73728\n",
      "w: [1.0018, 2.9494, 4.5371, 1.3722, 1.4422, 0.05, 1.2795, 0.1877, 0.8954, 1.8157, 0.5879, 0.5766, 1.5393]\n",
      "iteration: 147456\n",
      "w: [1.0018, 2.9494, 4.1733, 1.5979, 1.5198, 0.05, 1.2004, 0.15, 0.8753, 1.8466, 0.5755, 0.5188, 1.4228]\n",
      "Loss in trainset: 0.3136\n",
      "Loss in testset: 0.3268\n",
      "iteration: 221142\n",
      "w: [1.0018, 2.9494, 3.7874, 1.545, 1.434, 0.05, 1.1906, 0.15, 0.8935, 1.8138, 0.626, 0.4042, 1.4037]\n",
      "iteration: 294870\n",
      "w: [1.0018, 2.9494, 3.9491, 1.7074, 1.763, 0.05, 1.1782, 0.15, 0.9002, 1.8917, 0.5238, 0.6369, 1.4492]\n",
      "Loss in trainset: 0.3132\n",
      "Loss in testset: 0.3273\n",
      "iteration: 368556\n",
      "w: [1.0018, 2.9494, 3.6976, 1.4924, 1.4409, 0.05, 1.1352, 0.15, 0.8435, 1.8748, 0.5275, 0.5965, 1.386]\n",
      "iteration: 442284\n",
      "w: [1.0018, 2.9494, 3.8095, 1.7304, 1.5611, 0.05, 1.1802, 0.15, 0.8763, 1.7783, 0.6073, 0.5674, 1.2767]\n",
      "Loss in trainset: 0.3138\n",
      "Loss in testset: 0.3270\n",
      "iteration: 515970\n",
      "w: [1.0018, 2.9494, 3.7409, 1.577, 1.5448, 0.05, 1.177, 0.15, 0.8636, 1.853, 0.5232, 0.5963, 1.3224]\n",
      "iteration: 589698\n",
      "w: [1.0018, 2.9494, 3.7649, 1.5886, 1.5502, 0.05, 1.1698, 0.1674, 0.8482, 1.8428, 0.5259, 0.597, 1.315]\n",
      "Loss in trainset: 0.3130\n",
      "Loss in testset: 0.3268\n",
      "iteration: 663384\n",
      "w: [1.0018, 2.9494, 3.757, 1.5908, 1.5442, 0.05, 1.1668, 0.1504, 0.8409, 1.8256, 0.5401, 0.588, 1.3097]\n",
      "iteration: 737112\n",
      "w: [1.0018, 2.9494, 3.76, 1.5944, 1.5482, 0.05, 1.171, 0.1504, 0.8445, 1.8342, 0.531, 0.6006, 1.3129]\n",
      "Loss in trainset: 0.3129\n",
      "Loss in testset: 0.3267\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=5)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=5, lr=4e-2, batch_size=512)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2232, 2.7805, 3.803, 1.6765, 1.5517, 0.05, 1.1844, 0.1515, 0.8483, 1.8684, 0.5215, 0.5851, 1.1186]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,1d,2d,4d,8d,16d,1.0m,1.8m,3.2m,5.6m,9.3m\n",
      "difficulty history: 0,7.2,7.0,6.8,6.7,6.5,6.4,6.3,6.1,6.0,5.9\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,4d,9d,20d,1.4m,2.7m,5.0m,9.1m,1.3y,2.2y,3.6y\n",
      "difficulty history: 0,5.5,5.4,5.3,5.2,5.2,5.1,5.0,5.0,4.9,4.9\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,7d,18d,1.4m,3.1m,6.3m,1.0y,1.9y,3.3y,5.6y,9.3y\n",
      "difficulty history: 0,3.8,3.8,3.8,3.8,3.8,3.8,3.8,3.8,3.8,3.8\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,10d,28d,2.3m,5.4m,11.6m,1.9y,3.7y,6.6y,11.5y,19.3y\n",
      "difficulty history: 0,2.1,2.2,2.3,2.4,2.4,2.5,2.6,2.6,2.7,2.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(\"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]))\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([6.7842, 3.8030])\n",
      "tensor([17.6887,  3.8030])\n",
      "tensor([41.9432,  3.8030])\n",
      "tensor([91.6069,  3.8030])\n",
      "tensor([188.2489,   3.8030])\n",
      "tensor([16.5353,  6.7512])\n",
      "tensor([3.3351, 9.5520])\n",
      "tensor([4.5918, 9.2646])\n",
      "tensor([6.8964, 8.9915])\n",
      "tensor([10.3247,  8.7321])\n",
      "tensor([15.4351,  8.4856])\n",
      "tensor([23.3190,  8.2515])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,7,18,42,92,188,17,3,5,7,10,15,23\n",
      "difficulty history: 0,3.8,3.8,3.8,3.8,3.8,6.8,9.6,9.3,9.0,8.7,8.5,8.3\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.047943\n",
      "2     0.141912\n",
      "3     0.016003\n",
      "4     0.161717\n",
      "5     0.063101\n",
      "6     0.033536\n",
      "7     0.121507\n",
      "8     0.077521\n",
      "9     0.141925\n",
      "10    0.194835\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  939.95\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70bb728081ed4c418efba24b22c11d11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find optimal retention:   0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.86-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "minimum_stability = 0.1\n",
    "index_offset = -(np.log(minimum_stability) / np.log(base)).round().astype(int)\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 1e10\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return (np.log(stability) / np.log(base)).round().astype(int) + index_offset\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "terminal_stability = minimum_stability\n",
    "for _ in range(128):\n",
    "    terminal_stability = cal_next_recall_stability(terminal_stability, 0.96, 10, 1)\n",
    "index_len = stability2index(terminal_stability)\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2), desc=\"find optimal retention\"):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        iteration = 0\n",
    "        while diff > 1 and iteration < 2e5:\n",
    "            iteration += 1\n",
    "            total_time = time_list[d - 1].sum()\n",
    "            s_indices = np.arange(index_len - 2, -1, -1)\n",
    "            stabilities = stability_list[s_indices]\n",
    "            intervals = np.maximum(1, np.round(stabilities * np.log(recall) / np.log(0.9)))\n",
    "            p_recalls = np.power(0.9, intervals / stabilities)\n",
    "            recall_s = cal_next_recall_stability(stabilities, p_recalls, d, 1)\n",
    "            forget_d = np.minimum(d + d_offset, 10)\n",
    "            forget_s = cal_next_recall_stability(stabilities, p_recalls, forget_d, 0)\n",
    "            recall_s_indices = np.minimum(stability2index(recall_s), index_len - 1)\n",
    "            forget_s_indices = np.clip(stability2index(forget_s), 0, index_len - 1)\n",
    "            recall_times = time_list[d - 1][recall_s_indices] + r_time\n",
    "            forget_times = time_list[forget_d - 1][forget_s_indices] + f_time\n",
    "            exp_times = p_recalls * recall_times + (1.0 - p_recalls) * forget_times\n",
    "            mask = exp_times <= time_list[d - 1][s_indices]\n",
    "            time_list[d - 1][s_indices[mask]] = exp_times[mask]\n",
    "            diff = total_time - time_list[d - 1].sum()\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            if iteration % 1e5 == 0:\n",
    "                print(f\"iteration: {iteration}, diff: {diff}, s0_time: {s0_time}\")\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3373\n",
      "Loss after training: 0.3162\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9238\n",
      "RMSE: 0.0172\n",
      "[0.1362482  0.84675366]\n",
      "Last rating: 1\n",
      "R-squared: 0.2588\n",
      "RMSE: 0.0621\n",
      "[0.30777994 0.61924896]\n",
      "Last rating: 2\n",
      "R-squared: 0.3314\n",
      "RMSE: 0.0445\n",
      "[-0.12931155  1.09447398]\n",
      "Last rating: 3\n",
      "R-squared: 0.9319\n",
      "RMSE: 0.0180\n",
      "[0.05065254 0.95540554]\n",
      "Last rating: 4\n",
      "R-squared: 0.3161\n",
      "RMSE: 0.0333\n",
      "[0.32959948 0.66467486]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    print(f\"Last rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8b399_row0_col0, #T_8b399_row0_col1, #T_8b399_row0_col2, #T_8b399_row0_col3, #T_8b399_row0_col4, #T_8b399_row0_col5, #T_8b399_row0_col6, #T_8b399_row0_col7, #T_8b399_row0_col8, #T_8b399_row1_col0, #T_8b399_row1_col1, #T_8b399_row1_col2, #T_8b399_row1_col3, #T_8b399_row1_col4, #T_8b399_row1_col5, #T_8b399_row1_col6, #T_8b399_row1_col7, #T_8b399_row1_col8, #T_8b399_row2_col0, #T_8b399_row2_col1, #T_8b399_row2_col2, #T_8b399_row2_col3, #T_8b399_row2_col4, #T_8b399_row2_col5, #T_8b399_row2_col7, #T_8b399_row2_col8, #T_8b399_row3_col0, #T_8b399_row3_col1, #T_8b399_row3_col2, #T_8b399_row3_col3, #T_8b399_row3_col4, #T_8b399_row3_col5, #T_8b399_row3_col6, #T_8b399_row3_col8, #T_8b399_row4_col0, #T_8b399_row4_col1, #T_8b399_row4_col2, #T_8b399_row4_col3, #T_8b399_row4_col4, #T_8b399_row4_col5, #T_8b399_row5_col0, #T_8b399_row5_col1, #T_8b399_row5_col2, #T_8b399_row5_col3, #T_8b399_row5_col5, #T_8b399_row6_col0, #T_8b399_row6_col1, #T_8b399_row6_col2, #T_8b399_row6_col3, #T_8b399_row6_col5, #T_8b399_row7_col0, #T_8b399_row7_col1, #T_8b399_row7_col2, #T_8b399_row8_col0, #T_8b399_row8_col2, #T_8b399_row9_col0, #T_8b399_row9_col2, #T_8b399_row10_col2, #T_8b399_row11_col0, #T_8b399_row11_col2, #T_8b399_row12_col2, #T_8b399_row13_col2, #T_8b399_row13_col9, #T_8b399_row14_col2, #T_8b399_row14_col8, #T_8b399_row14_col9, #T_8b399_row15_col5, #T_8b399_row15_col6, #T_8b399_row15_col7, #T_8b399_row15_col8, #T_8b399_row15_col9, #T_8b399_row16_col1, #T_8b399_row16_col5, #T_8b399_row16_col6, #T_8b399_row16_col7, #T_8b399_row16_col8, #T_8b399_row16_col9, #T_8b399_row17_col1, #T_8b399_row17_col4, #T_8b399_row17_col5, #T_8b399_row17_col6, #T_8b399_row17_col7, #T_8b399_row17_col8, #T_8b399_row17_col9, #T_8b399_row18_col0, #T_8b399_row18_col1, #T_8b399_row18_col3, #T_8b399_row18_col4, #T_8b399_row18_col5, #T_8b399_row18_col6, #T_8b399_row18_col7, #T_8b399_row18_col8, #T_8b399_row18_col9 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row0_col9 {\n",
       "  background-color: #ff6161;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row1_col9, #T_8b399_row6_col4, #T_8b399_row9_col5, #T_8b399_row10_col7 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row2_col6 {\n",
       "  background-color: #ff8585;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row2_col9, #T_8b399_row10_col3, #T_8b399_row10_col6, #T_8b399_row14_col3 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row3_col7 {\n",
       "  background-color: #ff5959;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row3_col9, #T_8b399_row6_col6, #T_8b399_row9_col1, #T_8b399_row17_col2 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row4_col6, #T_8b399_row7_col3, #T_8b399_row9_col6, #T_8b399_row11_col4, #T_8b399_row12_col6 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row4_col7, #T_8b399_row16_col4 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row4_col8, #T_8b399_row5_col8 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row4_col9 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row5_col4, #T_8b399_row13_col6 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row5_col6, #T_8b399_row7_col7 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row5_col7, #T_8b399_row8_col6, #T_8b399_row12_col3 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row5_col9, #T_8b399_row8_col8, #T_8b399_row11_col8 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row6_col7, #T_8b399_row8_col4, #T_8b399_row10_col9, #T_8b399_row12_col4 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row6_col8, #T_8b399_row16_col2 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row6_col9 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row7_col4, #T_8b399_row7_col8, #T_8b399_row8_col1 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row7_col5, #T_8b399_row13_col0 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row7_col6 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row7_col9, #T_8b399_row13_col7 {\n",
       "  background-color: #ff9d9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row8_col3, #T_8b399_row11_col3 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row8_col5 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row8_col7, #T_8b399_row9_col4, #T_8b399_row13_col5 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row8_col9, #T_8b399_row14_col7 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row9_col3 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row9_col7, #T_8b399_row9_col8 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row9_col9, #T_8b399_row11_col5, #T_8b399_row12_col9 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row10_col0 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row10_col1 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row10_col4 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row10_col5, #T_8b399_row13_col4 {\n",
       "  background-color: #ffb5b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row10_col8, #T_8b399_row11_col9 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row11_col1, #T_8b399_row12_col1 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row11_col6, #T_8b399_row11_col7, #T_8b399_row12_col5 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row12_col0 {\n",
       "  background-color: #7575ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row12_col7 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row12_col8, #T_8b399_row15_col2 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row13_col1 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row13_col3 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row13_col8, #T_8b399_row14_col4, #T_8b399_row14_col6 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row14_col0, #T_8b399_row18_col2 {\n",
       "  background-color: #9999ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row14_col1, #T_8b399_row16_col3 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row14_col5 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row15_col0 {\n",
       "  background-color: #6d6dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row15_col1 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row15_col3 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row15_col4 {\n",
       "  background-color: #ffadad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8b399_row16_col0 {\n",
       "  background-color: #4141ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row17_col0 {\n",
       "  background-color: #1515ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8b399_row17_col3 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8b399\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_8b399_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_8b399_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_8b399_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_8b399_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_8b399_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_8b399_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_8b399_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_8b399_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_8b399_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_8b399_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row0\" class=\"row_heading level0 row0\" >0.510000</th>\n",
       "      <td id=\"T_8b399_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_8b399_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_8b399_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_8b399_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_8b399_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_8b399_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_8b399_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_8b399_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_8b399_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_8b399_row0_col9\" class=\"data row0 col9\" >6.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row1\" class=\"row_heading level0 row1\" >0.710000</th>\n",
       "      <td id=\"T_8b399_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_8b399_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_8b399_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_8b399_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_8b399_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_8b399_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_8b399_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_8b399_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_8b399_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_8b399_row1_col9\" class=\"data row1 col9\" >1.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row2\" class=\"row_heading level0 row2\" >1.000000</th>\n",
       "      <td id=\"T_8b399_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_8b399_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_8b399_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_8b399_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_8b399_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_8b399_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_8b399_row2_col6\" class=\"data row2 col6\" >4.82%</td>\n",
       "      <td id=\"T_8b399_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_8b399_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_8b399_row2_col9\" class=\"data row2 col9\" >-0.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row3\" class=\"row_heading level0 row3\" >1.400000</th>\n",
       "      <td id=\"T_8b399_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_8b399_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_8b399_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_8b399_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_8b399_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_8b399_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_8b399_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_8b399_row3_col7\" class=\"data row3 col7\" >6.48%</td>\n",
       "      <td id=\"T_8b399_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_8b399_row3_col9\" class=\"data row3 col9\" >-1.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row4\" class=\"row_heading level0 row4\" >1.960000</th>\n",
       "      <td id=\"T_8b399_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_8b399_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_8b399_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_8b399_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_8b399_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_8b399_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_8b399_row4_col6\" class=\"data row4 col6\" >0.87%</td>\n",
       "      <td id=\"T_8b399_row4_col7\" class=\"data row4 col7\" >-0.01%</td>\n",
       "      <td id=\"T_8b399_row4_col8\" class=\"data row4 col8\" >-1.94%</td>\n",
       "      <td id=\"T_8b399_row4_col9\" class=\"data row4 col9\" >-0.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row5\" class=\"row_heading level0 row5\" >2.740000</th>\n",
       "      <td id=\"T_8b399_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_8b399_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_8b399_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_8b399_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_8b399_row5_col4\" class=\"data row5 col4\" >3.41%</td>\n",
       "      <td id=\"T_8b399_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_8b399_row5_col6\" class=\"data row5 col6\" >0.07%</td>\n",
       "      <td id=\"T_8b399_row5_col7\" class=\"data row5 col7\" >0.22%</td>\n",
       "      <td id=\"T_8b399_row5_col8\" class=\"data row5 col8\" >-1.93%</td>\n",
       "      <td id=\"T_8b399_row5_col9\" class=\"data row5 col9\" >0.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row6\" class=\"row_heading level0 row6\" >3.840000</th>\n",
       "      <td id=\"T_8b399_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_8b399_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_8b399_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_8b399_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_8b399_row6_col4\" class=\"data row6 col4\" >1.63%</td>\n",
       "      <td id=\"T_8b399_row6_col5\" class=\"data row6 col5\" ></td>\n",
       "      <td id=\"T_8b399_row6_col6\" class=\"data row6 col6\" >-1.50%</td>\n",
       "      <td id=\"T_8b399_row6_col7\" class=\"data row6 col7\" >1.77%</td>\n",
       "      <td id=\"T_8b399_row6_col8\" class=\"data row6 col8\" >-0.78%</td>\n",
       "      <td id=\"T_8b399_row6_col9\" class=\"data row6 col9\" >2.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row7\" class=\"row_heading level0 row7\" >5.380000</th>\n",
       "      <td id=\"T_8b399_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_8b399_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_8b399_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_8b399_row7_col3\" class=\"data row7 col3\" >0.90%</td>\n",
       "      <td id=\"T_8b399_row7_col4\" class=\"data row7 col4\" >-0.70%</td>\n",
       "      <td id=\"T_8b399_row7_col5\" class=\"data row7 col5\" >-3.31%</td>\n",
       "      <td id=\"T_8b399_row7_col6\" class=\"data row7 col6\" >-1.37%</td>\n",
       "      <td id=\"T_8b399_row7_col7\" class=\"data row7 col7\" >0.14%</td>\n",
       "      <td id=\"T_8b399_row7_col8\" class=\"data row7 col8\" >-0.69%</td>\n",
       "      <td id=\"T_8b399_row7_col9\" class=\"data row7 col9\" >3.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row8\" class=\"row_heading level0 row8\" >7.530000</th>\n",
       "      <td id=\"T_8b399_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_8b399_row8_col1\" class=\"data row8 col1\" >-0.77%</td>\n",
       "      <td id=\"T_8b399_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_8b399_row8_col3\" class=\"data row8 col3\" >-1.17%</td>\n",
       "      <td id=\"T_8b399_row8_col4\" class=\"data row8 col4\" >1.80%</td>\n",
       "      <td id=\"T_8b399_row8_col5\" class=\"data row8 col5\" >-3.56%</td>\n",
       "      <td id=\"T_8b399_row8_col6\" class=\"data row8 col6\" >0.29%</td>\n",
       "      <td id=\"T_8b399_row8_col7\" class=\"data row8 col7\" >0.43%</td>\n",
       "      <td id=\"T_8b399_row8_col8\" class=\"data row8 col8\" >0.74%</td>\n",
       "      <td id=\"T_8b399_row8_col9\" class=\"data row8 col9\" >2.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row9\" class=\"row_heading level0 row9\" >10.540000</th>\n",
       "      <td id=\"T_8b399_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_8b399_row9_col1\" class=\"data row9 col1\" >-1.42%</td>\n",
       "      <td id=\"T_8b399_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_8b399_row9_col3\" class=\"data row9 col3\" >-2.10%</td>\n",
       "      <td id=\"T_8b399_row9_col4\" class=\"data row9 col4\" >0.46%</td>\n",
       "      <td id=\"T_8b399_row9_col5\" class=\"data row9 col5\" >1.62%</td>\n",
       "      <td id=\"T_8b399_row9_col6\" class=\"data row9 col6\" >0.81%</td>\n",
       "      <td id=\"T_8b399_row9_col7\" class=\"data row9 col7\" >1.88%</td>\n",
       "      <td id=\"T_8b399_row9_col8\" class=\"data row9 col8\" >1.89%</td>\n",
       "      <td id=\"T_8b399_row9_col9\" class=\"data row9 col9\" >3.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row10\" class=\"row_heading level0 row10\" >14.760000</th>\n",
       "      <td id=\"T_8b399_row10_col0\" class=\"data row10 col0\" >-4.23%</td>\n",
       "      <td id=\"T_8b399_row10_col1\" class=\"data row10 col1\" >-2.85%</td>\n",
       "      <td id=\"T_8b399_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "      <td id=\"T_8b399_row10_col3\" class=\"data row10 col3\" >-0.42%</td>\n",
       "      <td id=\"T_8b399_row10_col4\" class=\"data row10 col4\" >0.53%</td>\n",
       "      <td id=\"T_8b399_row10_col5\" class=\"data row10 col5\" >2.85%</td>\n",
       "      <td id=\"T_8b399_row10_col6\" class=\"data row10 col6\" >-0.37%</td>\n",
       "      <td id=\"T_8b399_row10_col7\" class=\"data row10 col7\" >1.61%</td>\n",
       "      <td id=\"T_8b399_row10_col8\" class=\"data row10 col8\" >2.23%</td>\n",
       "      <td id=\"T_8b399_row10_col9\" class=\"data row10 col9\" >1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row11\" class=\"row_heading level0 row11\" >20.660000</th>\n",
       "      <td id=\"T_8b399_row11_col0\" class=\"data row11 col0\" ></td>\n",
       "      <td id=\"T_8b399_row11_col1\" class=\"data row11 col1\" >-2.79%</td>\n",
       "      <td id=\"T_8b399_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_8b399_row11_col3\" class=\"data row11 col3\" >-1.19%</td>\n",
       "      <td id=\"T_8b399_row11_col4\" class=\"data row11 col4\" >0.81%</td>\n",
       "      <td id=\"T_8b399_row11_col5\" class=\"data row11 col5\" >3.03%</td>\n",
       "      <td id=\"T_8b399_row11_col6\" class=\"data row11 col6\" >3.54%</td>\n",
       "      <td id=\"T_8b399_row11_col7\" class=\"data row11 col7\" >3.55%</td>\n",
       "      <td id=\"T_8b399_row11_col8\" class=\"data row11 col8\" >0.73%</td>\n",
       "      <td id=\"T_8b399_row11_col9\" class=\"data row11 col9\" >2.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row12\" class=\"row_heading level0 row12\" >28.930000</th>\n",
       "      <td id=\"T_8b399_row12_col0\" class=\"data row12 col0\" >-5.32%</td>\n",
       "      <td id=\"T_8b399_row12_col1\" class=\"data row12 col1\" >-2.78%</td>\n",
       "      <td id=\"T_8b399_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "      <td id=\"T_8b399_row12_col3\" class=\"data row12 col3\" >0.25%</td>\n",
       "      <td id=\"T_8b399_row12_col4\" class=\"data row12 col4\" >1.83%</td>\n",
       "      <td id=\"T_8b399_row12_col5\" class=\"data row12 col5\" >3.56%</td>\n",
       "      <td id=\"T_8b399_row12_col6\" class=\"data row12 col6\" >0.85%</td>\n",
       "      <td id=\"T_8b399_row12_col7\" class=\"data row12 col7\" >3.68%</td>\n",
       "      <td id=\"T_8b399_row12_col8\" class=\"data row12 col8\" >1.41%</td>\n",
       "      <td id=\"T_8b399_row12_col9\" class=\"data row12 col9\" >3.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row13\" class=\"row_heading level0 row13\" >40.500000</th>\n",
       "      <td id=\"T_8b399_row13_col0\" class=\"data row13 col0\" >-3.40%</td>\n",
       "      <td id=\"T_8b399_row13_col1\" class=\"data row13 col1\" >-3.00%</td>\n",
       "      <td id=\"T_8b399_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "      <td id=\"T_8b399_row13_col3\" class=\"data row13 col3\" >-0.97%</td>\n",
       "      <td id=\"T_8b399_row13_col4\" class=\"data row13 col4\" >2.87%</td>\n",
       "      <td id=\"T_8b399_row13_col5\" class=\"data row13 col5\" >0.35%</td>\n",
       "      <td id=\"T_8b399_row13_col6\" class=\"data row13 col6\" >3.40%</td>\n",
       "      <td id=\"T_8b399_row13_col7\" class=\"data row13 col7\" >3.89%</td>\n",
       "      <td id=\"T_8b399_row13_col8\" class=\"data row13 col8\" >2.64%</td>\n",
       "      <td id=\"T_8b399_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row14\" class=\"row_heading level0 row14\" >56.690000</th>\n",
       "      <td id=\"T_8b399_row14_col0\" class=\"data row14 col0\" >-4.04%</td>\n",
       "      <td id=\"T_8b399_row14_col1\" class=\"data row14 col1\" >-2.20%</td>\n",
       "      <td id=\"T_8b399_row14_col2\" class=\"data row14 col2\" ></td>\n",
       "      <td id=\"T_8b399_row14_col3\" class=\"data row14 col3\" >-0.46%</td>\n",
       "      <td id=\"T_8b399_row14_col4\" class=\"data row14 col4\" >2.63%</td>\n",
       "      <td id=\"T_8b399_row14_col5\" class=\"data row14 col5\" >1.30%</td>\n",
       "      <td id=\"T_8b399_row14_col6\" class=\"data row14 col6\" >2.65%</td>\n",
       "      <td id=\"T_8b399_row14_col7\" class=\"data row14 col7\" >2.36%</td>\n",
       "      <td id=\"T_8b399_row14_col8\" class=\"data row14 col8\" ></td>\n",
       "      <td id=\"T_8b399_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row15\" class=\"row_heading level0 row15\" >79.370000</th>\n",
       "      <td id=\"T_8b399_row15_col0\" class=\"data row15 col0\" >-5.76%</td>\n",
       "      <td id=\"T_8b399_row15_col1\" class=\"data row15 col1\" >-3.83%</td>\n",
       "      <td id=\"T_8b399_row15_col2\" class=\"data row15 col2\" >1.49%</td>\n",
       "      <td id=\"T_8b399_row15_col3\" class=\"data row15 col3\" >-2.57%</td>\n",
       "      <td id=\"T_8b399_row15_col4\" class=\"data row15 col4\" >3.23%</td>\n",
       "      <td id=\"T_8b399_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_8b399_row15_col6\" class=\"data row15 col6\" ></td>\n",
       "      <td id=\"T_8b399_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_8b399_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "      <td id=\"T_8b399_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row16\" class=\"row_heading level0 row16\" >111.120000</th>\n",
       "      <td id=\"T_8b399_row16_col0\" class=\"data row16 col0\" >-7.41%</td>\n",
       "      <td id=\"T_8b399_row16_col1\" class=\"data row16 col1\" ></td>\n",
       "      <td id=\"T_8b399_row16_col2\" class=\"data row16 col2\" >-0.80%</td>\n",
       "      <td id=\"T_8b399_row16_col3\" class=\"data row16 col3\" >-2.27%</td>\n",
       "      <td id=\"T_8b399_row16_col4\" class=\"data row16 col4\" >-0.09%</td>\n",
       "      <td id=\"T_8b399_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_8b399_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_8b399_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_8b399_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_8b399_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row17\" class=\"row_heading level0 row17\" >155.570000</th>\n",
       "      <td id=\"T_8b399_row17_col0\" class=\"data row17 col0\" >-9.16%</td>\n",
       "      <td id=\"T_8b399_row17_col1\" class=\"data row17 col1\" ></td>\n",
       "      <td id=\"T_8b399_row17_col2\" class=\"data row17 col2\" >-1.54%</td>\n",
       "      <td id=\"T_8b399_row17_col3\" class=\"data row17 col3\" >-1.59%</td>\n",
       "      <td id=\"T_8b399_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_8b399_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_8b399_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_8b399_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_8b399_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_8b399_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b399_level0_row18\" class=\"row_heading level0 row18\" >217.800000</th>\n",
       "      <td id=\"T_8b399_row18_col0\" class=\"data row18 col0\" ></td>\n",
       "      <td id=\"T_8b399_row18_col1\" class=\"data row18 col1\" ></td>\n",
       "      <td id=\"T_8b399_row18_col2\" class=\"data row18 col2\" >-4.00%</td>\n",
       "      <td id=\"T_8b399_row18_col3\" class=\"data row18 col3\" ></td>\n",
       "      <td id=\"T_8b399_row18_col4\" class=\"data row18 col4\" ></td>\n",
       "      <td id=\"T_8b399_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_8b399_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_8b399_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_8b399_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_8b399_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b31fdcd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiffMax: 0.15760222041700522\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_rounded</th>\n",
       "      <th>D_rounded</th>\n",
       "      <th>R_t_rounded</th>\n",
       "      <th>R_measured</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.51</td>\n",
       "      <td>10</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.51</td>\n",
       "      <td>10</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.764075</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.71</td>\n",
       "      <td>10</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.867072</td>\n",
       "      <td>1482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.71</td>\n",
       "      <td>10</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.875417</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.00</td>\n",
       "      <td>7</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.733794</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>79.37</td>\n",
       "      <td>4</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.941896</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>111.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.950954</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>111.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.962871</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>155.57</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.947522</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>217.80</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.984416</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S_rounded  D_rounded  R_t_rounded  R_measured  count\n",
       "19         0.51         10        0.825    0.780000    400\n",
       "20         0.51         10        0.850    0.764075   1119\n",
       "37         0.71         10        0.875    0.867072   1482\n",
       "38         0.71         10        0.900    0.875417    899\n",
       "55         1.00          7        0.650    0.733794   1157\n",
       "...         ...        ...          ...         ...    ...\n",
       "1585      79.37          4        0.950    0.941896    327\n",
       "1672     111.12          1        0.875    0.950954    367\n",
       "1673     111.12          1        0.900    0.962871    404\n",
       "1791     155.57          3        0.950    0.947522    343\n",
       "1865     217.80          3        0.950    0.984416    385\n",
       "\n",
       "[183 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw['r_bin'] = B_W_Metric_raw['p'].map(lambda x: round(x * 4, 1) / 4)\n",
    "R_Matrix = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin', 'r_bin']).agg({'y': ['mean', 'count']}).reset_index()\n",
    "R_Matrix.columns = ['S_rounded', 'D_rounded', 'R_t_rounded', 'R_measured', 'count']\n",
    "R_Matrix = R_Matrix[R_Matrix['count'] > 300]\n",
    "print(\"DiffMax:\", max(abs(R_Matrix['R_t_rounded'] - R_Matrix['R_measured'])))\n",
    "R_Matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Comparision with Anki bulit-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anki(history):\n",
    "    ivl = 0\n",
    "    start_ease = 2.5\n",
    "    hard_interval = 1.2\n",
    "    ease_bonus = 1.3\n",
    "    graduating_interval = 1\n",
    "    easy_interval = 4\n",
    "    new_interval = 0\n",
    "    minimum_interval = 1\n",
    "    interval_modifier = 1\n",
    "    for i, (delta_t, rating) in enumerate(history):\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item()\n",
    "        if i == 0:\n",
    "            ease = start_ease\n",
    "            if rating == 4:\n",
    "                ivl = easy_interval\n",
    "            else:\n",
    "                ivl = graduating_interval\n",
    "        else:\n",
    "            delay = delta_t - ivl\n",
    "            if delay >= 0:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = ivl * hard_interval\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = (ivl + delay // 2) * ease\n",
    "                else:\n",
    "                    ivl = (ivl + delay) * ease * ease_bonus\n",
    "                    ease += 0.15\n",
    "            # early_review\n",
    "            else:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = max(delta_t * hard_interval / 2, ivl * hard_interval)\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = max(delta_t * ease, ivl)\n",
    "                else:\n",
    "                    ivl = max(delta_t * ease, ivl) * (0.5 * ease_bonus + 0.5)\n",
    "                    ease += 0.15\n",
    "            ivl = ivl * interval_modifier\n",
    "        ease = max(1.3, ease)\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.035699946156353246\n",
      "R-squared: -21.5980\n",
      "RMSE: 0.1408\n",
      "[0.76478601 0.15818509]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0138\n",
      "Universal Metric of SM2: 0.0672\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
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